A Hybrid RRT–PPO Framework for Leg-Based Object Manipulation of Quadruped Robots
TL;DR
A hybrid framework that couples Rapidly exploring Random Trees (RRT) for global motion planning with Proximal Policy Optimization (PPO) for local leg-pushing control is presented, evaluated in a CoppeliaSim simulation of a Spot-like quadruped pushing a box to a goal pose.
Abstract
Quadruped robots can extend their utility beyond locomotion by using a leg as a non-prehensile end-effector to push objects, but this requires combining global planning with adaptive contact control. We present a hybrid framework that couples Rapidly exploring Random Trees (RRT) for global motion planning with Proximal Policy Optimization (PPO) for local leg-pushing control, evaluated in a CoppeliaSim simulation of a Spot-like quadruped pushing a box to a goal pose. The PPO action consists of Bézier control-point parameters and a leg-selection index, and the reward combines positional error, angular error, and a stability penalty. The agent learns straight-line pushing. Without retraining, a fixed asymmetric Bézier action induces a consistent rotation at ω≈4–5×10−4 rad/s, and the resulting circular arcs are composed—in the spirit of Dubins paths—to follow curved trajectories. For box masses from 0.1 kg to 0.9 kg (up to eight times the training mass), angular and position errors grew approximately linearly from 0.59° to 6.32° and 0.18 m to 0.64 m, respectively, with no abrupt divergence. A single learned pushing primitive, combined with sampling-based planning and a deterministic composition rule, generates both straight-line and curved manipulation.