Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation

Nolan Fey, Gabriel B. Margolis, Martin Peticco, Pulkit Agrawal

Track: system paper (must be submitted with a supplementary video)

Keywords: Sim-to-Real Transfer, System Identification, Reinforcement Learning, Loco-Manipulation

TL;DR: We propose a sim-to-real reinforcement learning pipeline to enable athletic behaviors on a quadruped robot equipped with an arm.

Abstract: Achieving athletic loco‑manipulation on robots requires moving beyond traditional tracking rewards—which simply guide the robot along a reference trajectory—to task rewards that drive truly dynamic, goal-oriented behaviors. Commands such as “throw the ball as far as you can” or “lift the weight as quickly as possible” compel the robot to exhibit the agility and power inherent in athletic performance. However, training solely with task rewards introduces two major challenges: these rewards are prone to exploitation (reward hacking), and the exploration process can lack sufficient direction. To address these issues, we propose a two‑stage training pipeline. First, we introduce the Unsupervised Actuator Net (UAN), which leverages real‑world data to bridge the sim-to-real gap for complex actuation mechanisms without requiring access to torque sensing. UAN mitigates reward hacking by ensuring that the learned behaviors remain robust and transferable. Second, we use a pre‑training and fine‑tuning strategy that leverages reference trajectories as initial hints to guide exploration. With these innovations, our robot athlete learns to lift, throw, and drag with remarkable fidelity from simulation to reality.

Presenter: ~Nolan_Fey1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 53