閉ループアフィン活性化編集によるマルチロボット行動の操縦
Steering Multirobot Behavior via Closed-Loop Affine Activation Editing
凍結したポリシーの活性化を閉ループで編集し、追加学習なしでロボットの行動を操縦するフレームワークCLAEを提案。マルチクアッドローターのナビゲーションで、個体の速度制御や編隊維持、新規行動の生成を実証した。
詳しい要約
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著者: Satyajeet Das, Darren Chiu, Shashank Hegde, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
Real-world robots need to adapt their behavior beyond the envelope of their pre-trained policy. Policy finetuning or retraining are options, but they risk catastrophic forgetting, degrading the pretrained policy's base performance. To combat this, we introduce CLAE: Closed-Loop Affine Activation Editing, an inference-time framework for steering the behavior of a frozen policy by editing intermediate activations while keeping the base policy weights and downstream action head untouched. CLAE approaches behavior steering as a closed-loop problem whose outputs edit policy activations that adapt online to the robot state, environment, target behavior, and multi-robot context. It trains a sparse autoencoder over frozen-policy activations, selects behavior-relevant latent features via post-hoc probing, and learns a lightweight RL-based steering policy that applies state-dependent affine edits to selected latents during inference. We validate CLAE on a frozen multi-quadrotor navigation policy trained to perform a single task: navigating robots to a set of goal locations while avoiding obstacles. Through extensive simulations and physical tests, we show that while navigating to their goal positions, CLAE can 1. steer individual robot behavior by controlling each robot's velocity profile; 2. coordinate multirobot behavior by preserving a desired formation; and 3. produce entirely new behavior wherein robots are required to reduce their exposure to surveillance cameras in the environment.
