Mousumi Das

SWAP casts robot policy routing as an offline reinforcement learning problem, training an IQL routing critic that picks, after each action chunk, the candidate pretrained VLA policy with the highest predicted long-horizon return given the current observation; on real-world DROID tabletop manipulation it raises success from 63.3% for the best single policy to 96.7% while cutting average action steps for successful trajectories from 269 to 193 (a 28.3% reduction), and it outperforms fixed-policy and random-routing baselines in LIBERO and LIBERO-Plus simulation.

SWAP casts robot policy routing as an offline reinforcement learning problem, training an IQL routing critic that picks, after each action chunk, the candidate pretrained VLA policy with the highest predicted long-horizon return given the current observation; on real-world DROID tabletop manipulation it raises success from 63.3% for the best single policy to 96.7% while cutting average action steps for successful trajectories from 269 to 193 (a 28.3% reduction), and it outperforms fixed-policy and random-routing baselines in LIBERO and LIBERO-Plus simulation.