134 citations · 137 across the 7 of their papers we have counts for
8 papers
Diffusion-based Planning with Learned Viability Filters
Nicholas Ioannidis, Daniele Reda, Setareh Cohan +1
Diffusion models can be used as a motion planner by sampling from a distribution of possible futures. However, the samples may not satisfy hard constraints that exist only implicit…
Learning-based legged locomotion; state of the art and future perspectives
Sehoon Ha, Joonho Lee, Michiel van de Panne +3
Legged locomotion holds the premise of universal mobility, a critical capability for many real-world robotic applications. Both model-based and learning-based approaches have advan…
Hierarchical Planning and Control for Box Loco-Manipulation
Zhaoming Xie, Jonathan Tseng, Sebastian Starke +2
Humans perform everyday tasks using a combination of locomotion and manipulation skills. Building a system that can handle both skills is essential to creating virtual humans. We p…
OPT-Mimic: Imitation of Optimized Trajectories for Dynamic Quadruped Behaviors
Yuni Fuchioka, Zhaoming Xie, Michiel van de Panne
Reinforcement Learning (RL) has seen many recent successes for quadruped robot control. The imitation of reference motions provides a simple and powerful prior for guiding solution…
Learning to Get Up
Tianxin Tao, Matthew Wilson, Ruiyu Gou +1
Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not re…
Learning Task-Agnostic Action Spaces for Movement Optimization
Amin Babadi, Michiel van de Panne, C. Karen Liu +1
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Lik…
Model-Based Action Exploration for Learning Dynamic Motion Skills
Glen Berseth, Michiel van de Panne
Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gat…
Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?
Xue Bin Peng, Michiel van de Panne
The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts the learning difficu…