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ProactiveVLA: Augmenting Embodied Memory through Proactive Environment Exploration

Shizuo Tian, Haodong Luo, Yutong Li, Yuebing Song, Yunxin Liu, Yuanchun Li

arXiv:2610.06999v1cs.RO

TL;DR

Rapid adaptation requires knowledge of local objects, action outcomes, and interaction sequences beyond the conditions already encountered. ProactiveVLA explores after an initial task and consolidates task-directed and exploratory experience into memory. It improves task execution over continued task refinement on LIBERO-Pro and RoboCasa365 Composite-Seen, particularly when few VLA calls are available.

  • Problem

    Rapid adaptation in unfamiliar environments requires experience about local object uses, action outcomes, and interaction sequences beyond conditions already encountered.

    Source: §1 · pp. 1–2

  • Method

    ProactiveVLA uses remaining interaction turns for three levels of self-proposed exploration and consolidates task and exploration experience into memory while keeping the VLA frozen.

    Source: §1 · p. 2, §3 · p. 3, §3.2 · p. 4

  • Results

    ProactiveVLA improves task execution over continued task refinement on LIBERO-Pro and RoboCasa365 Composite-Seen, with particularly strong benefits when few VLA calls are available.

    Source: §1 · p. 2, §7 · p. 10

  • Takeaways & Limitations

    The results suggest that adaptation depends not only on accumulating experience but also on actively acquiring and organizing knowledge to guide subsequent behavior.

    Source: §7 · p. 10

    Experiments are simulated, so transfer to real-world perception, dynamics, and execution noise remains unevaluated.

    Source: §6 · p. 9

Abstract

from arXiv · show

Rapid adaptation to a new environment requires a robot to acquire useful knowledge about local objects, states, and interactions from limited experience. Systems that combine a reasoning agent with a frozen vision-language-action model (VLA) can adapt through execution feedback and memory, making the choice of experience central to their effectiveness. Repeated practice of a target task may refine a familiar solution while leaving other interactions relevant to changed conditions untested. We introduce ProactiveVLA, which uses proactive environment exploration to acquire reusable knowledge for deployment-time adaptation. After completing an initial task, the agent allocates the remaining interaction budget to self-proposed goals covering object affordances, state-changing interactions, and compositions of interactions. It verifies execution outcomes and consolidates both task-directed and exploratory experience into memory that guides subsequent planning and control. ProactiveVLA outperforms the baselines under the same turn budget on LIBERO-Pro and RoboCasa365 Composite-Seen. On LIBERO-Pro Goal-T, with at most one VLA primitive invocation allowed during evaluation, ProactiveVLA completes 48% of instances, compared with 19% for the state-of-the-art task-refinement baseline.

1 INTRODUCTION

Rapid adaptation requires experience about local objects, action outcomes, and interaction sequences that goes beyond conditions already encountered. ProactiveVLA uses the remaining interaction budget for structured exploration and consolidates that experience with task-directed experience to guide later execution.

1 INTRODUCTION

  • Task refinement improves execution while keeping the provided objective fixed, so its experience may remain concentrated around familiar task requirements.For example, unobstructed placement practice may not provide evidence about clearing space when a target becomes obstructed.Source: §1 · p. 1
  • Expanding the conditions under which a robot can act and improving a familiar solution are different uses of the same limited interaction budget.Proactive exploration targets reusable knowledge about object roles, interaction effects, and combinations relevant to changes in tasks and execution conditions.Source: §1 · p. 2
  • ProactiveVLA explores object affordances, state-changing interactions, and interaction compositions after attempting the target task, then consolidates both sources of experience into memory.An independent critic reviews proposed rules before they guide execution, while the VLA remains frozen.Source: §1 · p. 2
  • The paper evaluates ProactiveVLA on LIBERO-Pro and RoboCasa365 Composite-Seen, covering task and spatial variations as well as multi-step household tasks.It analyzes how exploration structure and memory consolidation affect adaptation.Source: §1 · p. 2

2 RELATED WORK

Prior work addresses VLA generalization, agentic adaptation, and reusable experience through exploration and memory. ProactiveVLA focuses on which experience an agent should acquire from a new environment under a limited interaction budget.

Vision-language-action models and generalization

  • VLA models gain manipulation capabilities through pretraining, but robustness can decrease when deployment changes task bindings, object arrangements, or compositional requirements.LIBERO-Pro evaluates controlled task perturbations, while RoboCasa365 provides diverse multi-step kitchen manipulation tasks.Source: §2 · p. 2

Agentic adaptation around robot policies

  • Agentic systems combine language-model reasoning with perception and control interfaces, and Harness VLA pairs a frozen VLA primitive with analytic control and execution memory.ProactiveVLA studies acquiring reusable environment knowledge rather than concentrating additional experience on a single task.Source: §2 · pp. 2–3

Autonomous exploration and reusable experience

  • Exploration systems such as Plan2Explore, ELLM, and Voyager acquire experience beyond a single externally specified task, while memory systems use reflection, verification, or distilled strategies.ProactiveVLA shares the goal of reusable knowledge but studies a budgeted preparation problem.Source: §2 · p. 3

3 METHODOLOGY

ProactiveVLA first attempts the provided task, then uses remaining interaction turns to explore and consolidate experience into global memory. During deployment, that fixed memory guides task execution while the VLA remains frozen.

3 METHODOLOGY

  • The method has three stages: exploration gathers task-directed and self-directed experience, memory management consolidates it across scenes, and deployment uses the resulting global memory.Each adaptation instance has a scene, an environment-provided task, and a shared turn budget.Source: §3 · p. 3

3.1 PROACTIVE ENVIRONMENT EXPLORATION

  • After attempting the target task, the agent spends remaining turns on goals at three levels: object affordances, state-changing interactions, and interaction compositions.An independent visual checker assesses outcomes and feeds back into exploration; the agent retains successful, failed, and uncertain attempts, including recovery behavior.Source: §3.1 · pp. 3–4, Figure 1 · p. 4

3.2 MEMORY MANAGEMENT

  • Memory management consolidates experience from the provided task and self-proposed goals into accepted knowledge synthesized across adaptation scenes.The process archives and atomizes trajectories, induces scene-level knowledge, synthesizes candidate rules across scenes, and independently reviews those rules against action evidence and visual assessments.Source: §3.2 · pp. 4–5

3.3 MEMORY-GUIDED TASK EXECUTION

  • At evaluation, the agent combines global memory with the task instruction and current observations to guide object selection, changed states, and intermediate conditions.Both global memory and VLA parameters remain fixed throughout evaluation.Source: §3.3 · p. 5

4 EXPERIMENTS

The experiments evaluate ProactiveVLA under fixed interaction budgets on LIBERO-Pro and RoboCasa365 Composite-Seen, with matched Harness VLA comparisons and component ablations. ProactiveVLA leads overall on LIBERO-Pro, improves across most settings, and outperforms Harness VLA on RoboCasa365 Composite-Seen.

4 EXPERIMENTS

  • Both methods receive the same reference instance before evaluation, and Harness VLA uses the matched Codex planner as the task-refinement baseline.ProactiveVLA uses remaining budget for self-proposed scene goals, verifies outcomes visually, and consolidates experience into memory.Source: §4 · p. 5, §4.1 · p. 6

4.2 OVERALL RESULTS

  • ProactiveVLA achieves the highest overall LIBERO-Pro success and improves over Harness VLA (Codex) in seven of eight settings.Table 1 reports success rates across the eight settings; Cap-X and RATS averages use only their six reported entries.Source: Table 1 · p. 6, §4.2 · p. 7

4.1 EXPERIMENTAL SETUP

  • LIBERO-Pro tests Spatial, Object, Goal, and LIBERO-10 under instruction redirection and position swaps, using eight settings with ten tasks and ten rollouts per task.Its overall score is the unweighted mean of the eight settings; Cap-X and RATS report only six settings.Source: §4.1 · p. 6
  • RoboCasa365 Composite-Seen evaluates 16 multi-step kitchen tasks, with five seeded rollouts per task and a 30-agent-turn budget per rollout.All 16 task classes appear in benchmark pretraining data, while evaluation uses target instances.Source: §4.1 · p. 6

4.2 OVERALL RESULTS

  • All four position-swap settings improve, while the largest gain is on Goal-T, which redirects the requested objective.Position swap changes object placement while keeping the instruction fixed.Source: §4.1 · p. 6, §4.2 · p. 7
  • ProactiveVLA outperforms Harness VLA on RoboCasa365 Composite-Seen, with gains concentrated in several multi-step household tasks.The evaluated benchmark includes 16 tasks; Table 2 reports per-task success rates.Source: §4.2 · p. 7, Table 2 · p. 7

4.3 ABLATIONS

  • Removing initial task guidance or memory management causes a moderate reduction, while removing three-level exploration produces the largest degradation.The ablations compare skipping task guidance, using raw exploration records without consolidation, and free-form exploration without three-level goal constraints.Source: §4.3 · p. 7, Table 3 · p. 8
  • With few VLA calls available, ProactiveVLA has its largest advantage over Harness VLA, and three-level exploration contributes most to this low-budget capability.Figure 2 analyzes task success against the maximum number of VLA primitive invocations allowed during evaluation.Source: Figure 2 · p. 8

5 ANALYSIS

ProactiveVLA’s proactive exploration supports more efficient execution with few VLA calls and provides recovery behavior for changed conditions. Its controller use is also more fine-grained than Harness VLA’s.

5 ANALYSIS

  • 48% of LIBERO-Pro Goal-T instances are completed with at most one VLA call by ProactiveVLA, compared with 19% for Harness VLA.Perception and analytic control remain available before and after each evaluation-time VLA call.Source: §5 · p. 8
  • Under target occlusion, ProactiveVLA re-localizes before resuming and succeeds, while Harness VLA acts at the wrong position and fails.The corresponding memory rule is to move away from the occlusion, localize the object again, and then continue.Source: Figure 3 · p. 9, §5 · p. 9
  • Action-level instructions account for 63.4% of ProactiveVLA’s VLA calls versus 34.7% for Harness VLA, while task-level instructions account for 0.6% versus 27.3%.After a VLA call, ProactiveVLA transitions to move_to in 24.7% of cases, compared with 8.4% for Harness VLA.Source: §5 · p. 9

6 LIMITATIONS

The evaluation leaves real-world transfer untested, uses one agentic VLA execution stack, and fixes the interaction budget.

6 LIMITATIONS

  • Experiments use simulated manipulation environments, so transfer to real-world perception, dynamics, and execution noise remains unevaluated.The limitation is the untested extent of transfer, not a claim that transfer fails.Source: §6 · p. 9
  • The current implementation uses a single agentic VLA execution stack, leaving effectiveness with other planners and low-level controllers unestablished.Source: §6 · p. 9
  • Evaluation uses fixed interaction budgets, and different amounts or distributions of adaptation experience may change the relative benefit of proactive exploration and task refinement.Source: §6 · p. 9

7 CONCLUSION

ProactiveVLA uses remaining interaction budget for structured exploration and consolidates experience into global memory while keeping the VLA frozen. Experiments show improved task execution over continued task refinement on LIBERO-Pro and RoboCasa365 Composite-Seen, especially with few VLA calls.

7 CONCLUSION

  • After the initial task, ProactiveVLA uses the remaining interaction budget for structured proactive exploration and consolidates successful, failed, and recovery experience into global memory.The VLA remains frozen.Source: §7 · p. 9
  • ProactiveVLA improves task execution over continued task refinement on LIBERO-Pro and RoboCasa365 Composite-Seen, with particular benefits when only a few VLA calls are available.Source: §7 · p. 10
  • The conclusion frames effective adaptation as actively acquiring and organizing environment knowledge to guide subsequent behavior, rather than merely accumulating experience.Source: §7 · p. 10

AI USE STATEMENT

Generative AI tools supported several research stages, including ideation, experiments, literature research, and manuscript preparation. The authors report reviewing and verifying AI-assisted content and take responsibility for the paper.

AI USE STATEMENT

  • Generative AI tools assisted research ideation, experimental design and implementation, analysis of intermediate results, and literature retrieval and summarization.Source: AI USE STATEMENT · p. 10
  • Generative AI produced synthetic experimental data, including the exploration task and memory generation described in the paper.Source: AI USE STATEMENT · p. 10
  • AI tools helped draft and revise manuscript text, and the authors say they reviewed, revised, and verified AI-assisted material before inclusion and take responsibility for the paper’s content.Source: AI USE STATEMENT · p. 10

A LIBERO-PRO GOAL-S TASKS

Table 4 identifies the tasks used in the LIBERO-Pro Goal-S evaluation and maps them to the task IDs reported in Table 3.

A LIBERO-PRO GOAL-S TASKS

B PROMPTS USED BY PROACTIVEVLA

The prompts direct ProactiveVLA to acquire task-specific experience first, explore the scene through verified goals, and organize evidence into reusable memory for later evaluation.

B.1 PROACTIVE ENVIRONMENT EXPLORATION

  • The agent first focuses on the provided task, then uses its remaining budget to explore object affordances, state changes, and verified interaction compositions.Source: §B.1 · p. 15
  • Autonomous goals should use clear, task-local visual evidence, and memory is generated only after independent verification of completion.Source: §B.1 · pp. 15–16
  • After failed attempts, the agent changes its approach and continues the same task while budgets remain, finalizing only after verification, budget exhaustion, or confirmed irreversible error.Source: §B.1 · p. 16

B.2 MEMORY MANAGEMENT

  • Experience is organized as object affordances, action skills, and long-horizon task connections; cross-scene confidence increases only with multiple evidence pieces or a success-failure contrast.Source: §B.2 · p. 17
  • The independent memory reviewer accepts evidence-supported, reusable observations with reasonable scope, while speculative or insufficiently supported claims remain hypotheses.Source: §B.2 · p. 17
  • Memory regeneration preserves atomic source items, conditions, evidence, failures, and recovery details rather than compressing them into generic principles.Source: §B.2 · p. 18

B.3 EVALUATION-TIME MEMORY INJECTION

  • At evaluation time, the full frozen ProactiveVLA memory is injected as the run’s sole memory source.Source: §B.3 · p. 18

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