254 citations · 1.4k across the 100 of their papers we have counts for
18 papers · 1 filter
Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam, Samarth Sinha, Homanga Bharadhwaj +6
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy…
Chemistry Lab Automation via Constrained Task and Motion Planning
Naruki Yoshikawa, Andrew Zou Li, Kourosh Darvish +6
Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
nerf2nerf: Pairwise Registration of Neural Radiance Fields
Lily Goli, Daniel Rebain, Sara Sabour +2
We introduce a technique for pairwise registration of neural fields that extends classical optimization-based local registration (i.e. ICP) to operate on Neural Radiance Fields (Ne…
Breaking Bad: A Dataset for Geometric Fracture and Reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu +2
We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset consists of over one million fractured objects simulated from ten thousand base models. The fract…
MoCoDA: Model-based Counterfactual Data Augmentation
Silviu Pitis, Elliot Creager, Ajay Mandlekar +1
The number of states in a dynamic process is exponential in the number of objects, making reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scal…
See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction
Maria Attarian, Advaya Gupta, Ziyi Zhou +3
Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors. In the computational setting, performing cognitive planning entails groundi…
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
Ziyi Wu, Nikita Dvornik, Klaus Greff +2
Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent…
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Ishika Singh, Valts Blukis, Arsalan Mousavian +6
Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to sco…
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Dylan Turpin, Liquan Wang, Eric Heiden +5
The study of hand-object interaction requires generating viable grasp poses for high-dimensional multi-finger models, often relying on analytic grasp synthesis which tends to produ…
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam +3
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, plann…
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Mohan Zhang, Xiaozhou Wang, Benjamin Decardi-Nelson +9
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds. Modernized factories have advanced process control algorit…
Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors
Yun-Chun Chen, Haoda Li, Dylan Turpin +2
Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic p…
Accelerated Policy Learning with Parallel Differentiable Simulation
Jie Xu, Viktor Makoviychuk, Yashraj Narang +4
Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue…
Value Gradient weighted Model-Based Reinforcement Learning
Claas Voelcker, Victor Liao, Animesh Garg +1
Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead performance deterioration. The mode…
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval
Satya Krishna Gorti, Noel Vouitsis, Junwei Ma +4
In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. H…
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
Eric Heiden, Miles Macklin, Yashraj Narang +3
Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators c…
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang +4
Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline…