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cs.RO2026
Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
Feng Tao, Luca Paparusso, Chenyi Gu +6
Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception…
cs.RO2023
Learning a Single Policy for Diverse Behaviors on a Quadrupedal Robot using Scalable Motion Imitation
Arnaud Klipfel, Nitish Sontakke, Ren Liu +1
Learning various motor skills for quadrupedal robots is a challenging problem that requires careful design of task-specific mathematical models or reward descriptions. In this work…
cs.RO2021
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Ren Liu, Nitish Sontakke, Sehoon Ha
Deep reinforcement learning (deep RL) has emerged as an effective tool for developing controllers for legged robots. However, vanilla deep RL often requires a tremendous amount of…