Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running
Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running explores Enabling humanoid robots to run dynamically and autonomously by retargeting human motion and using goal-conditioned reinforcement learning.. Commercial viability score: 7/10 in Robotics Control.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisRoboForge: Physically Optimized Text-guided Whole-Body Locomotion for HumanoidsViability: 3.0
- Builds On ThisLearning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement LearningViability: 5.0
- Builds On ThisCost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid LocomotionViability: 5.0
- Prior WorkHybridMimic: Hybrid RL-Centroidal Control for Humanoid Motion MimickingViability: 7.0
- Prior WorkSPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization