49 citations · 76 across the 3 of their papers we have counts for
8 papers
Learning Latent Actions to Control Assistive Robots
Dylan P. Losey, Hong Jun Jeon, Mengxi Li +5
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use…
Dexterous Manipulation Primitives for the Real Robot Challenge
Claire Chen, Krishnan Srinivasan, Jeffrey Zhang +7
This report describes our approach for Phase 3 of the Real Robot Challenge. To solve cuboid manipulation tasks of varying difficulty, we decompose each task into the following prim…
Learning to be Safe: Deep RL with a Safety Critic
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha +2
Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first a…
Learning Hierarchical Control for Robust In-Hand Manipulation
Tingguang Li, Krishnan Srinivasan, Max Qing-Hu Meng +2
Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipula…
Controlling Assistive Robots with Learned Latent Actions
Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar +2
Assistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms use…
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Peter Zachares +6
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines th…