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Critic in the Loop: A Tri-System VLA Framework for Robust Long-Horizon Manipulation
This paper proposes a tri-system Vision-Language-Action (VLA) framework that introduces a visual Critic for dynamic scheduling between high-level VLM planning and low-level VLA control, improving real-time performance…
vision-language-actionlong-horizon-manipulationhierarchical-controlanomaly-detectionood-generalizationrobot-policy
Summary
This paper proposes a tri-system Vision-Language-Action (VLA) framework that introduces a visual Critic for dynamic scheduling between high-level VLM planning and low-level VLA control, improving real-time performance and robustness in long-horizon manipulation. The core idea is to activate the slow reasoning module only when needed, thereby achieving stronger performance under OOD disturbances, stagnation, and failure recovery.
Problem
- Existing hierarchical VLA systems often rigidly chain together a slow but semantically capable VLM with a fast but semantically weaker VLA, leading to inflexible switching, wasted computation, and slow responses to disturbances.
- In long-horizon manipulation, robots are prone to stagnation, misgrasping, dropping objects, and infinite retries; relying on collecting dedicated data for each such failure scales poorly.
- This matters because real-world robots need both high-level semantic planning and low-level real-time closed-loop control, especially in complex, open, and OOD settings.
Approach
- The paper proposes Tri-System: System 2 is the VLM “Brain” responsible for generating semantic subtasks, System 1 is the flow-matching “Cerebellum” responsible for continuous actions, and System 3 is a lightweight visual Critic that monitors execution and decides when to switch.
- The Critic unifies subtask evaluation as VQA text generation: it outputs either a progress value (discretizing completion into 101 bins corresponding to the interval [-1,0]) or the anomaly token
<aci>, thereby handling both progress tracking and failure detection. - Scheduling is event-driven and asynchronous: under normal conditions, the VLA continuously executes at around 20Hz; the VLM is triggered for replanning and the old action cache is cleared only when a subtask is completed, an accident is detected, or prolonged stagnation occurs.
- To break infinite retry loops, the system incorporates human-inspired heuristic rules: if the Critic finds that progress has not improved for a long time (e.g., maximum stagnation threshold
N_stag=180frames), it resets the robot state and lets the Brain replan based on short-term memory. - The paper also proposes an automatic subtask annotation pipeline: it first uses end-effector trajectories and gripper states for keyframe proposals, then uses a VLM to retrieve semantic labels, reducing the cost of manual segment-by-segment annotation.
Results
- On the real-robot Arrange the Tableware task, Tri-System achieves 10/10, 9/10, 7/10, 7/10 in the four scenarios Ordered / Scattered / Left cup / Fallen, outperforming Single-System at 8/10, 0/10, 0/10, 2/10 and Dual-System at 7/10, 6/10, 1/10, 5/10.
- On the more complex long-horizon Tidy up the Desk task, Tri-System achieves stage-wise success counts of Open 9/10, Bottle1 8/10, Bottle2 5/10, Overall 4/10; the corresponding Single-System results are 7/10, 5/10, 2/10, 0/10, and Dual-System results are 6/10, 5/10, 1/10, 0/10.
- The paper claims the method achieves state-of-the-art across all evaluation scenarios, especially in the OOD left-cup scenario, where the training data contains no left-arm samples for that task, yet Tri-System still reaches 7/10, significantly higher than Dual-System’s 1/10 and Single-System’s 0/10.
- At the system runtime level, the authors report key mechanism figures: the control/observation loop runs at about 20 Hz; an example success threshold is
τ_succ ≈ -0.041; the stagnation threshold isN_stag=180; and the Critic uses Florence-2-base with about 0.2B parameters to support real-time asynchronous evaluation. - For training data, 200 teleoperated trajectories are collected for each task; the tableware arrangement task additionally includes 100 trajectories for recovery after “the cup is knocked over.” Despite these data, the authors specifically emphasize that using the left arm for the left cup remains an out-of-distribution test.
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