Human-Aware Robot Navigation in Dynamic Crowds using Reinforcement Learning
Socially compliant robot navigation in dense human crowds using reinforcement learning and interaction-aware rewards.
Objective
Build a navigation policy that remains safe, efficient, and socially compliant in dense (specially if group of human’s are also present), dynamic crowds.
My role
- Designed the RL formulation (state, reward, training protocol)
- Integrated perception + policy + planning into a usable navigation stack
- Built evaluation scenarios across multiple crowd densities
Methods
- First we build a modular reactive based group avodance algorithm named TAGA(A Tangent-Based Group Avoidance Controller)
- Then moved to RL policy training (sim-to-sim evaluation)
- Reward shaping for comfort + safety + efficiency
- Metrics: Group Collision rate(our proposal metric for group navigation performance)
Results (high-level)
- Summarize best-performing policy settings
- Key tradeoffs observed (speed vs comfort, etc.)
Media
In website many results are shown. So, please visit the website for see the results.