most citedLearning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

13 citations · 29 across the 6 of their papers we have counts for

collaborators
Showing cs.ROShow all

5 papers · 1 filter

cs.RO20233 cited

RoboHive: A Unified Framework for Robot Learning

Vikash Kumar, Rutav Shah, Gaoyue Zhou +5

We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a di…

cs.RO202310 cited

Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models

Pierre Schumacher, Thomas Geijtenbeek, Vittorio Caggiano +4

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to…

cs.RO20233 cited

MyoDex: A Generalizable Prior for Dexterous Manipulation

Vittorio Caggiano, Sudeep Dasari, Vikash Kumar

Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled b…

cs.RO2023

RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking

Homanga Bharadhwaj, Jay Vakil, Mohit Sharma +3

The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets. Acquiring and growing such datase…

cs.RO202313 cited

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Tony Z. Zhao, Vikash Kumar, Sergey Levine +1

Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact fo…