Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation

Learns a unified position-and-force policy for legged robots. Simulated reinforcement learning uses state history to estimate forces and compensate motion, supporting contact-rich manipulation without force sensors.

Learns a unified position-and-force policy for legged robots. Simulated reinforcement learning uses state history to estimate forces and compensate motion, supporting contact-rich manipulation without force sensors.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Learns a unified position-and-force policy for legged robots. Simulated reinforcement learning uses state history to estimate forces and compensate motion, supporting contact-rich manipulation without force sensors.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.