109 citations · 270 across the 14 of their papers we have counts for
3 papers · 1 filter
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman +7
The goal of continuous control is to synthesize desired behaviors. In reinforcement learning (RL)-driven approaches, this is often accomplished through careful task reward engineer…
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Michael H. Lim, Andy Zeng, Brian Ichter +5
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and generalizability, scaling th…
Fast and Efficient Locomotion via Learned Gait Transitions
Yuxiang Yang, Tingnan Zhang, Erwin Coumans +2
We focus on the problem of developing energy efficient controllers for quadrupedal robots. Animals can actively switch gaits at different speeds to lower their energy consumption.…