60 citations · 171 across the 17 of their papers we have counts for
20 papers · 1 filter
OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation
Adithyavairavan Murali, Yash Oza, Karanbir Chahal +6
Handheld data-collection interfaces such as the Universal Manipulation Interface (UMI) allow manipulation demonstrations to be gathered in the wild without a robot in the loop, but…
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
Kaiyuan Chen, Shuangyu Xie, Letian Fu +21
For robots to work reliably in commercial and industrial applications, recent advances in agentic coding systems have potential to combine interpretable robot programming with the…
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
Beining Han, Yu-Wei Chao, Erwin Coumans +5
We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and…
cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots
Balakumar Sundaralingam, Adithyavairavan Murali, Stan Birchfield
Effective robot autonomy requires motion generation that is safe, feasible, and reactive. Current methods are fragmented: fast planners output physically unexecutable trajectories,…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
Jun Yamada, Adithyavairavan Murali, Ajay Mandlekar +3
Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered enviro…
GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao +7
Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize acro…
DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning
Huang Huang, Balakumar Sundaralingam, Arsalan Mousavian +3
Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all…
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics
Wentao Yuan, Jiafei Duan, Valts Blukis +5
From rearranging objects on a table to putting groceries into shelves, robots must plan precise action points to perform tasks accurately and reliably. In spite of the recent adopt…
M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
Wentao Yuan, Adithyavairavan Murali, Arsalan Mousavian +1
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation. These generic m…
CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner +2
We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes - orders of magni…
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…
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang +6
We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasp…
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
Adithyavairavan Murali, Weiyu Liu, Kenneth Marino +2
Despite the enormous progress and generalization in robotic grasping in recent years, existing methods have yet to scale and generalize task-oriented grasping to the same extent. T…
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner +2
Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulato…
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
Adithyavairavan Murali, Tao Chen, Kalyan Vasudev Alwala +4
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking. PyRobot is a light-weight, high-level interface on top of ROS that provides a consis…
Hardware Conditioned Policies for Multi-Robot Transfer Learning
Tao Chen, Adithyavairavan Murali, Abhinav Gupta
Deep reinforcement learning could be used to learn dexterous robotic policies but it is challenging to transfer them to new robots with vastly different hardware properties. It is…
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Abhinav Gupta, Adithyavairavan Murali, Dhiraj Gandhi +1
Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in cura…
Learning to Grasp Without Seeing
Adithyavairavan Murali, Yin Li, Dhiraj Gandhi +1
Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prio…
CASSL: Curriculum Accelerated Self-Supervised Learning
Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi +1
Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framewor…