ArticleScientific reports2024
Learning agility and adaptive legged locomotion via curricular hindsight reinforcement learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- TSIE: Robust Blind Locomotion Learning for Bipedal Robots via Terrain and State Implicit-Explicit Estimation.Biomimetics (Basel, Switzerland) · 2026Article
- The Influence of Different Cognitive Skills on Learning Agility Among Gen Z in Established and Start-Up Companies.Behavioral sciences (Basel, Switzerland) · 2026Article
- An integrated tiny-YOLO v3 and Q-iteration framework for stable, energy-efficient autonomous navigation of quadruped robots on AMB82-mini microcontrollers.Scientific reports · 2025Article
- Imitation learning for legged robot locomotion: a survey.Frontiers in robotics and AI · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (i) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor running speeds up to 3.45 m/s, and a maximum yaw rate of 3.2 rad/s. This system produces adaptive behaviors responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.