arXiv (All)
open access
Synergistic Action and Style Priors for Learning Legged Locomotion
Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning#1386238
- Mind the Phase: Effective Rank and Representation Health in Legged Locomotion#975032
- GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots#1049676
- eRLT: Efficient VLA Reinforcement Learning via Action-Relevant Token Routing#1155848
- From POMDP Theory to Deep RL with Particle Filters#1403371
Record · ID 1402904
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive