2 citations · 4 across the 3 of their papers we have counts for
9 papers · 1 filter
URDF+: An Enhanced URDF for Robots with Kinematic Loops
Matthew Chignoli, Jean-Jacques Slotine, Patrick M. Wensing +1
Designs incorporating kinematic loops are becoming increasingly prevalent in the robotics community. Despite the existence of dynamics algorithms to deal with the effects of such l…
Probabilistic Homotopy Optimization for Dynamic Motion Planning
Shayan Pardis, Matthew Chignoli, Sangbae Kim
We present a homotopic approach to solving challenging, optimization-based motion planning problems. The approach uses Homotopy Optimization, which, unlike standard continuation me…
CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control
Se Hwan Jeon, Seungwoo Hong, Ho Jae Lee +2
The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into thi…
Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion
Ho Jae Lee, Seungwoo Hong, Sangbae Kim
In this work, we introduce a control framework that combines model-based footstep planning with Reinforcement Learning (RL), leveraging desired footstep patterns derived from the L…
Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback
Jenny Zhang, Steve Heim, Se Hwan Jeon +1
We present a minimal phase oscillator model for learning quadrupedal locomotion. Each of the four oscillators is coupled only to itself and its corresponding leg through local feed…
Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
Se Hwan Jeon, Steve Heim, Charles Khazoom +1
The main challenge in developing effective reinforcement learning (RL) pipelines is often the design and tuning the reward functions. Well-designed shaping reward can lead to signi…