NVIDIA AI 发布 ASPIRE：自我改进机器人框架，在 LIBERO-Pro 长任务上零样本达到 31%

传统机器人编程很难规模化，因为需要手工编排多模态感知、物理接触动态、不同配置和执行失败处理。ASPIRE 尝试通过自我改进框架提升机器人长任务表现。ms let language models compose these into executable robot programs. That makes robot behavior inspectable, editable, and debuggable.

But existing robotic coding agents run in naive execution environments. They receive only coarse, task-level feedback. A failed rollout signals that the task failed, not why. The root cause can be perception, motion planning, grasping, contact dynamics, or long-horizon coordination. These systems also discard fixes once a task ends. So the agent solving its hundredth task is no more experienced than at its first.

A team of researchers from NVIDIA, University of Michigan, UIUC, UC Berkeley, and CMU introduces ASPIRE (Agentic Skill Programming through Iterative Robot Exploration). It is a continual learning system that writes and refines robot control programs. It also distills validated fixes into a reusable, transferable skill library.

How ASPIRE works

ASPIRE runs an open-ended learning loop with three components. It uses a coordinator–actor architecture. A central coordinator manages the shared skill library and dispatches actor coding agents to tasks. Actors do not exchange full chat histories or raw trajectories. Only distilled skills move between them.

Closed-loop robot execution engine: This replaces coarse rollout feedback with per-primitive multimodal traces. For each perception, planning, and control call, it stores inputs, outputs, and return status. It also stores RGB keyframes, overlays, grasp candidates, object poses, and motion-planning results. The agent inspects only the calls implicated by a failure. It then localizes the fault and validates a repair through re-execution.

Skill library: Reusable knowledge is rarely an entire task program. So the library stores heterogeneous fixes. These include localization heuristics, perception prompts, grasping constraints, motion primitives, and debugging workflows. Each skill is compact in-context guidance. It holds a failure signature, a when-to-apply condition, a repair strategy, and often a code sketch. The coordinator admits only patterns that pass debug validation and API-policy checks.

Evolutionary search: Trace-guided debugging alone can collapse into local repair loops. The agent keeps patching the same failed strategy. To broaden exploration, ASPIRE proposes K candidate programs each round. Candidates condition on top-performing prior programs and their remaining failure traces. The next round explores distinct strategies rather than refining one solution.

In simulation, the coding agent is Claude Code with Claude Opus 4.6 and a 1M-token context window. Programs are written in CaP-X, an open-source code-as-policy framework built on MuJoCo Playground. The agent cannot read simulator ground truth. Reading physics-engine state or asset files like .bddl, .xml, or .urdf is forbidden. The rule is simple. If a real robot with a camera could do it, it is allowed.

Interactive Explainer

A worked example: the Multi-Angle Approach skill

Consider a BEHAVIOR-1K task where a robot must pick up a radio near a table. Perception returns the radio pose, but repeated navigate_to_pose calls fail. The generated goal lies within about 20 centimeters of the table edge. That falls inside the table’s collision-avoidance buffer, and cuRobo returns PLANNING_ERROR.

The agent reads the trace and localizes the cause. The failure is target infeasibility, not perception or grasping. It then writes a repair that samples standoff poses around the radio.

# radio_pos, safe_navigate() and dist_to() are provided by ASPIRE's robot API
for angle_deg in [180, -90, 90, -45, 45]:
    angle = np.radians(angle_deg)
    tx = radio_pos[0] + 0.7 * np.cos(angle)      # standoff 0.7 m from the radio
    ty = radio_pos[1] + 0.7 * np.sin(angle)
    face_yaw = np.arctan2(radio_pos[1] - ty, radio_pos[0] - tx)
    moved = safe_navigate([tx, ty, face_yaw], f"ang_{angle_deg}")
    if moved and dist_to(radio_pos[:2]) < 0.8:   # reached a pose within 0.8 m
        break

Each angle puts the goal on a different side of the object. When one side is blocked, another is often open. Here the 180-degree pose clears the buffer. The validated fix is admitted as a reusable navigation-recovery skill.

Benchmarks and results

ASPIRE is evaluated on three benchmark families. LIBERO-Pro tests short-horizon robustness under object, goal, and spatial perturbations. Robosuite covers contact-rich single- and dual-arm manipulation. BEHAVIOR-1K covers long-horizon household mobile manipulation. The primary coding-agent baseline is CaP-Agent0. It uses visual differencing, a predefined skill library, and per-episode test-time retries. The comparison also includes end-to-end vision-language-action policies: OpenVLA, π0, and π0.5.

On LIBERO-Pro, ASPIRE gains up to 77 points on the Object suite. That figure averages both perturbation axes over the strongest baseline. It also gains 41.5 points on Goal and 42.5 points on Spatial. On Robosuite, bimanual handover rises from 20% to 92%. On BEHAVIOR-1K, the radio pickup task rises from 56% to 88%.

The zero-shot result is notable. Reusing skills accumulated on LIBERO-90, ASPIRE reaches about 31% on held-out LIBERO-Pro Long tasks. Prior methods saturate near 4%.

Real-robot skill transfer

The research team tests three simulation-discovered skills on a real bimanual YAM station. The real-robot coding agent is OpenAI Codex GPT-5.5. The embodiment and API differ from simulation. Transferred skills reduce debugging cost. Soda-can lifting improved from 13/20 to 19/20 while using about 10x fewer tokens. Drawer opening moved from 0/20 to 11/20, where the no-skill baseline never succeeded.

Key Takeaways

• ASPIRE writes and debugs robot programs, then saves validated fixes as reusable in-context skills.
• Per-primitive multimodal traces let the agent localize failures instead of guessing from rollout outcomes.
• It gains up to 77 points on LIBERO-Pro and lifts Robosuite handover from 20% to 92%.
• Zero-shot transfer reaches about 31% on LIBERO-Pro Long, against about 4% for prior methods.
• Simulation-discovered skills reduced real-robot debugging cost across a different embodiment and API.

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