48 citations · 237 across the 29 of their papers we have counts for
5 papers · 1 filter
Cross-Domain Transfer via Semantic Skill Imitation
Karl Pertsch, Ruta Desai, Vikash Kumar +4
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL) in a different target do…
Transformers are Adaptable Task Planners
Vidhi Jain, Yixin Lin, Eric Undersander +2
Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-…
Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation
Joanne Truong, Max Rudolph, Naoki Yokoyama +3
If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simu…
Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots
Chenyu Yang, Guo Ning Sue, Zhongyu Li +6
This paper tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (…
Learning Torque Control for Quadrupedal Locomotion
Shuxiao Chen, Bike Zhang, Mark W. Mueller +2
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots. Conventionally, an RL design for locomotion follows a position-based p…