2k citations · 3k across the 41 of their papers we have counts for
43 papers
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…
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
Patrick Lancaster, Nicklas Hansen, Aravind Rajeswaran +1
Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminat…
SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation
Cameron Berg, Vittorio Caggiano, Vikash Kumar
Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evoluti…
LIV: Language-Image Representations and Rewards for Robotic Control
Yecheng Jason Ma, William Liang, Vaidehi Som +4
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting…
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…
GenAug: Retargeting behaviors to unseen situations via Generative Augmentation
Zoey Chen, Sho Kiami, Abhishek Gupta +1
Robot learning methods have the potential for widespread generalization across tasks, environments, and objects. However, these methods require large diverse datasets that are expe…