Publications (91)
FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection
Harry Zhang, Ben Eisner, David Held
Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to…
Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions
Alexis Yihong Hao, Yufei Wang, Navin Sriram Ravie +3
Robot-assisted dressing has the potential to significantly improve the lives of individuals with mobility impairments. To ensure an effective and comfortable dressing experience, t…
Real-World Offline Reinforcement Learning from Vision Language Model Feedback
Sreyas Venkataraman, Yufei Wang, Ziyu Wang +3
Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety…
ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation
Yufei Wang, Ziyu Wang, Mino Nakura +5
This paper presents ArticuBot, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. This task has…
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects
Ben Eisner, Harry Zhang, David Held
We explore a novel method to perceive and manipulate 3D articulated objects that generalizes to enable a robot to articulate unseen classes of objects. We propose a vision-based sy…
RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation
Yufei Wang, Zhou Xian, Feng Chen +6
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in fou…
Cloth Region Segmentation for Robust Grasp Selection
Jianing Qian, Thomas Weng, Luxin Zhang +2
Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many…
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Yufei Wang, Gautham Narayan Narasimhan, Xingyu Lin +2
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling a…
Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
Zixuan Huang, Xingyu Lin, David Held
Self-occlusion is challenging for cloth manipulation, as it makes it difficult to estimate the full state of the cloth. Ideally, a robot trying to unfold a crumpled or folded cloth…
3D-DLP: Self-Supervised 3D Object-Centric Scene Representation Learning
Ellina Zhang, Madhaven Iyengar, Amir Zadeh +4
We introduce 3D-DLP, a self-supervised object-centric representation learning model that decomposes scene-level RGB-D or voxel observations into a set of 3D latent particles. Build…
Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement
Kallol Saha, Amber Li, Angela Rodriguez-Izquierdo +4
Long-horizon planning for robot manipulation is a challenging problem that requires reasoning about the effects of a sequence of actions on a physical 3D scene. While traditional t…
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
Alberta Longhini, Marco Moletta, Alfredo Reichlin +4
We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of ela…
Few-Shot Point Cloud Region Annotation with Human in the Loop
Siddhant Jain, Sowmya Munukutla, David Held
We propose a point cloud annotation framework that employs human-in-loop learning to enable the creation of large point cloud datasets with per-point annotations. Sparse labels fro…
On Time-Indexing as Inductive Bias in Deep RL for Sequential Manipulation Tasks
M. Nomaan Qureshi, Ben Eisner, David Held
While solving complex manipulation tasks, manipulation policies often need to learn a set of diverse skills to accomplish these tasks. The set of skills is often quite multimodal -…
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
Ben Eisner, Yi Yang, Todor Davchev +3
Many robot manipulation tasks can be framed as geometric reasoning tasks, where an agent must be able to precisely manipulate an object into a position that satisfies the task from…
AutoBag: Learning to Open Plastic Bags and Insert Objects
Lawrence Yunliang Chen, Baiyu Shi, Daniel Seita +4
Thin plastic bags are ubiquitous in retail stores, healthcare, food handling, recycling, homes, and school lunchrooms. They are challenging both for perception (due to specularitie…
Differentiable Raycasting for Self-supervised Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, Achal Dave +3
Motion planning for safe autonomous driving requires learning how the environment around an ego-vehicle evolves with time. Ego-centric perception of driveable regions in a scene no…
Unfolding the Literature: A Review of Robotic Cloth Manipulation
Alberta Longhini, Yufei Wang, Irene Garcia-Camacho +9
The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on…
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking
Zixuan Huang, Xingyu Lin, David Held
State estimation is one of the greatest challenges for cloth manipulation due to cloth's high dimensionality and self-occlusion. Prior works propose to identify the full state of c…
Lyapunov Barrier Policy Optimization
Harshit Sikchi, Wenxuan Zhou, David Held
Deploying Reinforcement Learning (RL) agents in the real-world require that the agents satisfy safety constraints. Current RL agents explore the environment without considering the…
Deep Projective Rotation Estimation through Relative Supervision
Brian Okorn, Chuer Pan, Martial Hebert +1
Orientation estimation is the core to a variety of vision and robotics tasks such as camera and object pose estimation. Deep learning has offered a way to develop image-based orien…
Learning Off-Policy with Online Planning
Harshit Sikchi, Wenxuan Zhou, David Held
Reinforcement learning (RL) in low-data and risk-sensitive domains requires performant and flexible deployment policies that can readily incorporate constraints during deployment.…
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, David Held +1
Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations…
PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection
Xia Chen, Jianren Wang, David Held +1
Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure.…
RoboWits: Unexpected Challenges for Robotic Creative Problem Solving
Chunru Lin, Hongxin Zhang, Fenghao Yu +5
The ability to reason, adapt, and creatively solve problems under unexpected challenges is essential for robots operating in real-world environments. However, current robotic bench…
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Tal Daniel, Carl Qi, Dan Haramati +5
We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM aut…
ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds
Daniel Seita, Yufei Wang, Sarthak J. Shetty +3
Point clouds are a widely available and canonical data modality which convey the 3D geometry of a scene. Despite significant progress in classification and segmentation from point…
HACMan++: Spatially-Grounded Motion Primitives for Manipulation
Bowen Jiang, Yilin Wu, Wenxuan Zhou +2
Although end-to-end robot learning has shown some success for robot manipulation, the learned policies are often not sufficiently robust to variations in object pose or geometry. T…
Enabling Robots to Communicate their Objectives
Sandy H. Huang, David Held, Pieter Abbeel +1
The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct resu…
Visual Haptic Reasoning: Estimating Contact Forces by Observing Deformable Object Interactions
Yufei Wang, David Held, Zackory Erickson
Robotic manipulation of highly deformable cloth presents a promising opportunity to assist people with several daily tasks, such as washing dishes; folding laundry; or dressing, ba…
PCN: Point Completion Network
Wentao Yuan, Tejas Khot, David Held +2
Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we…
Deep Learning for Single-View Instance Recognition
David Held, Sebastian Thrun, Silvio Savarese
Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small numbe…
Self-Supervised Point Cloud Completion via Inpainting
Himangi Mittal, Brian Okorn, Arpit Jangid +1
When navigating in urban environments, many of the objects that need to be tracked and avoided are heavily occluded. Planning and tracking using these partial scans can be challeng…
RB2: Robotic Manipulation Benchmarking with a Twist
Sudeep Dasari, Jianren Wang, Joyce Hong +12
Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research g…
Bagging by Learning to Singulate Layers Using Interactive Perception
Lawrence Yunliang Chen, Baiyu Shi, Roy Lin +6
Many fabric handling and 2D deformable material tasks in homes and industry require singulating layers of material such as opening a bag or arranging garments for sewing. In contra…
Learning Visible Connectivity Dynamics for Cloth Smoothing
Xingyu Lin, Yufei Wang, Zixuan Huang +1
Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions. In con…
Non-rigid Relative Placement through 3D Dense Diffusion
Eric Cai, Octavian Donca, Ben Eisner +1
The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric rea…
Active Safety Envelopes using Light Curtains with Probabilistic Guarantees
Siddharth Ancha, Gaurav Pathak, Srinivasa G. Narasimhan +1
To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of…
Reverse Curriculum Generation for Reinforcement Learning
Carlos Florensa, David Held, Markus Wulfmeier +2
Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gea…
Reinforcement Learning without Ground-Truth State
Xingyu Lin, Harjatin Singh Baweja, David Held
To perform robot manipulation tasks, a low-dimensional state of the environment typically needs to be estimated. However, designing a state estimator can sometimes be difficult, es…
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Yufei Wang, Zhanyi Sun, Jesse Zhang +4
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to desi…
Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity
Wenxuan Zhou, David Held
A simple gripper can solve more complex manipulation tasks if it can utilize the external environment such as pushing the object against the table or a vertical wall, known as "Ext…
Geometric Red-Teaming for Robotic Manipulation
Divyam Goel, Yufei Wang, Tiancheng Wu +4
Standard evaluation protocols in robotic manipulation typically assess policy performance over curated, in-distribution test sets, offering limited insight into how systems fail un…
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
Jianren Wang, Haiming Gang, Siddharth Ancha +2
3D object detection plays an important role in autonomous driving and other robotics applications. However, these detectors usually require training on large amounts of annotated d…
Visual Manipulation with Legs
Xialin He, Chengjing Yuan, Wenxuan Zhou +3
Animals use limbs for both locomotion and manipulation. We aim to equip quadruped robots with similar versatility. This work introduces a system that enables quadruped robots to in…
Elastic Context: Encoding Elasticity for Data-driven Models of Textiles
Alberta Longhini, Marco Moletta, Alfredo Reichlin +6
Physical interaction with textiles, such as assistive dressing, relies on advanced dextreous capabilities. The underlying complexity in textile behavior when being pulled and stret…
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Xingyu Lin, Zhiao Huang, Yunzhu Li +3
We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to…
AB3DMOT: A Baseline for 3D Multi-Object Tracking and New Evaluation Metrics
Xinshuo Weng, Jianren Wang, David Held +1
3D multi-object tracking (MOT) is essential to applications such as autonomous driving. Recent work focuses on developing accurate systems giving less attention to computational co…
Learning to Track at 100 FPS with Deep Regression Networks
David Held, Sebastian Thrun, Silvio Savarese
Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic ob…
FlowBotHD: History-Aware Diffuser Handling Ambiguities in Articulated Objects Manipulation
Yishu Li, Wen Hui Leng, Yiming Fang +2
We introduce a novel approach for manipulating articulated objects which are visually ambiguous, such doors which are symmetric or which are heavily occluded. These ambiguities can…
Just Go with the Flow: Self-Supervised Scene Flow Estimation
Himangi Mittal, Brian Okorn, David Held
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular…
Uncertainty-aware Self-supervised 3D Data Association
Jianren Wang, Siddharth Ancha, Yi-Ting Chen +1
3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datase…
Force-Constrained Visual Policy: Safe Robot-Assisted Dressing via Multi-Modal Sensing
Zhanyi Sun, Yufei Wang, David Held +1
Robot-assisted dressing could profoundly enhance the quality of life of adults with physical disabilities. To achieve this, a robot can benefit from both visual and force sensing.…
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…
HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation
Wenxuan Zhou, Bowen Jiang, Fan Yang +2
Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more com…
ZePHyR: Zero-shot Pose Hypothesis Rating
Brian Okorn, Qiao Gu, Martial Hebert +1
Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulati…
FusionMapping: Learning Depth Prediction with Monocular Images and 2D Laser Scans
Peng Yin, Jianing Qian, Yibo Cao +2
Acquiring accurate three-dimensional depth information conventionally requires expensive multibeam LiDAR devices. Recently, researchers have developed a less expensive option by pr…
TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation
Chuer Pan, Brian Okorn, Harry Zhang +2
How do we imbue robots with the ability to efficiently manipulate unseen objects and transfer relevant skills based on demonstrations? End-to-end learning methods often fail to gen…
What You See is What You Get: Exploiting Visibility for 3D Object Detection
Peiyun Hu, Jason Ziglar, David Held +1
Recent advances in 3D sensing have created unique challenges for computer vision. One fundamental challenge is finding a good representation for 3D sensor data. Most popular repres…
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
Sriram Krishna, Ben Eisner, Haotian Zhan +5
We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy…
Learning Closed-loop Dough Manipulation Using a Differentiable Reset Module
Carl Qi, Xingyu Lin, David Held
Deformable object manipulation has many applications such as cooking and laundry folding in our daily lives. Manipulating elastoplastic objects such as dough is particularly challe…
Learning Distributional Demonstration Spaces for Task-Specific Cross-Pose Estimation
Jenny Wang, Octavian Donca, David Held
Relative placement tasks are an important category of tasks in which one object needs to be placed in a desired pose relative to another object. Previous work has shown success in…
Probabilistically Safe Policy Transfer
David Held, Zoe McCarthy, Michael Zhang +2
Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the…
Learning Generalizable Tool-use Skills through Trajectory Generation
Carl Qi, Yilin Wu, Lifan Yu +4
Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching hu…
One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments
Yufei Wang, Zhanyi Sun, Zackory Erickson +1
Robot-assisted dressing could benefit the lives of many people such as older adults and individuals with disabilities. Despite such potential, robot-assisted dressing remains a cha…
3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
Xinshuo Weng, Jianren Wang, David Held +1
3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accura…
Robust Instance Tracking via Uncertainty Flow
Jianing Qian, Junyu Nan, Siddharth Ancha +2
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes. In this work, we explore the use ofdense optical flow to improve tracking robust…
Learning Orientation Distributions for Object Pose Estimation
Brian Okorn, Mengyun Xu, Martial Hebert +1
For robots to operate robustly in the real world, they should be aware of their uncertainty. However, most methods for object pose estimation return a single point estimate of the…
Disentangled Point Diffusion for Precise Object Placement
Lyuxing He, Eric Cai, Shobhit Aggarwal +2
Recent advances in robotic manipulation have highlighted the effectiveness of learning from demonstration. However, while end-to-end policies excel in expressivity and flexibility,…
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa, David Held, Xinyang Geng +1
Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the…
Adaptive Variance for Changing Sparse-Reward Environments
Xingyu Lin, Pengsheng Guo, Carlos Florensa +1
Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled…
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits
Siddharth Ancha, Gaurav Pathak, Ji Zhang +2
To navigate in an environment safely and autonomously, robots must accurately estimate where obstacles are and how they move. Instead of using expensive traditional 3D sensors, we…
Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving
Pranay Gupta, Abhijat Biswas, Henny Admoni +1
The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It a…
Neural Grasp Distance Fields for Robot Manipulation
Thomas Weng, David Held, Franziska Meier +1
We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a co…
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Xingyu Lin, Yufei Wang, Jake Olkin +1
Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement le…
Iterative Transformer Network for 3D Point Cloud
Wentao Yuan, David Held, Christoph Mertz +1
3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on 3D understanding…
Combining Deep Learning and Verification for Precise Object Instance Detection
Siddharth Ancha, Junyu Nan, David Held
Deep learning object detectors often return false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), t…
OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation
Qiao Gu, Brian Okorn, David Held
Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of…
SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting
Mohammad Nomaan Qureshi, Sparsh Garg, Francisco Yandun +3
Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and…
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy
Thomas Weng, Sujay Bajracharya, Yufei Wang +2
We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for mo…
Active Perception using Light Curtains for Autonomous Driving
Siddharth Ancha, Yaadhav Raaj, Peiyun Hu +2
Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this w…
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Ying Yuan, Xinyu Liu, Sriram Krishna +1
Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pick-and-place manipulation. W…
DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning
Weikang Wan, Ziyu Wang, Yufei Wang +2
This paper introduces DiffTORI, which utilizes Differentiable Trajectory Optimization as the policy representation to generate actions for deep Reinforcement and Imitation learning…
Multi-modal Transfer Learning for Grasping Transparent and Specular Objects
Thomas Weng, Amith Pallankize, Yimin Tang +2
State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To i…
Learning to Singulate Layers of Cloth using Tactile Feedback
Sashank Tirumala, Thomas Weng, Daniel Seita +3
Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to thei…
Learn from What We HAVE: History-Aware VErifier that Reasons about Past Interactions Online
Yishu Li, Xinyi Mao, Ying Yuan +3
We introduce a novel History-Aware VErifier (HAVE) to disambiguate uncertain scenarios online by leveraging past interactions. Robots frequently encounter visually ambiguous object…
Learning to Optimally Segment Point Clouds
Peiyun Hu, David Held, Deva Ramanan
We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it sea…
PLAS: Latent Action Space for Offline Reinforcement Learning
Wenxuan Zhou, Sujay Bajracharya, David Held
The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more i…
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization
Fan Yang, Wenxuan Zhou, Zuxin Liu +2
Safe Reinforcement Learning (RL) plays an important role in applying RL algorithms to safety-critical real-world applications, addressing the trade-off between maximizing rewards a…
Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation
Xingyu Lin, Carl Qi, Yunchu Zhang +5
Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels. Previous methods typically either focus on…
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Gautham Narayan Narasimhan, Kai Zhang, Ben Eisner +2
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentat…