cs.ROOct 30, 2026
SaveMETA-Robot: Self-Cognition and Capability Scaling in Agentic Robots
Organizations: The Hong Kong University of Science and Technology · University of Macau
Abstract
LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill repertoires alone offers limited guidance on how to assess new capabilities or identify what remains beyond their reach. We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. At its core, action-level predicates make execution outcomes explicit and available for LLM reasoning. The agent uses these assessments to identify its own limitations, extend relevant evaluation criteria, and guide the structured expansion of its skills. Proprioceptive feedback further grounds this process in the robot's physical state, enriching the evidence available for self-cognition. Experiments in simulation and on real robots demonstrate stronger overall task performance, generalization to new tasks, continued expansion of skills and evaluation criteria, and effective use of non-visual proprioceptive signals.
Figures & tables
Figure 1: META-Robot starts from a task description. Its framework consists of three components: (1) Loop Execution Agent, which generates programs and predicates, evaluates execution evidence, and repairs failures; (2) Proprioceptive Signal Orchestration, which supplements insufficient visual observations with physical feedback; and (3) coupled Predicate and Skill Libraries, which consolidate validated criteria and repairs for cognitive and capability scaling.
Figure 2: META-Robot Loop Execution Agent. Given a task description, the agent (1) decomposes it into executable subtasks and initial conditions; (2) jointly generates the task program and action-level predicates; (3) executes each skill while collecting task-relevant evidence; (4) evaluates predicate transitions to determine whether execution should continue or be repaired; (5) locally corrects and re-executes the failed skill, missing evidence routine, or verification check; and (6) performs system-level repair and full-task validation, consolidating validated predicates and skills for reuse. This procedure is repeated to form a closed execution–evaluation–repair loop.
Figure 3: Proprioceptive Signal Orchestration Mechanism Example: in thin-towel grasping, torque change provides a contact cue, while gripper feedback and a lift check verify successful acquisition to complement insufficient visual evidence during execution and verification. Validated signal usage is then consolidated into a reusable skill together with corresponding action predicates, jointly expanding sensing, execution, and verification capabilities
| Category | Task Type | OpenVLA | CaP- Agent0 | ASPIRE | Playful / RATS | META-Robot (DeepSeek- V4-Flash) | META-Robot (GPT-6 Astra) | ||
|---|---|---|---|---|---|---|---|---|---|
| Object | Pos | 0.00 | 0.00 | 17.00 | 22.00 | 98.00 | 61.00 | 98.00 | 99.60 |
| Task | 0.00 | 0.00 | 1.00 | 18.00 | 95.00 | 63.00 | 88.60 | 99.00 | |
| Goal | Pos | 0.00 | 0.00 | 38.00 | 26.00 | 81.00 | 43.00 | 72.20 | 85.80 |
| Task | 0.00 | 0.00 | 0.00 | 17.00 | 45.00 | 36.00 | 64.60 | 69.80 | |
| Spatial | Pos | 0.00 | 0.00 | 20.00 | 12.00 | 51.00 | 29.00 | 83.00 | 89.25 |
| Task | 0.00 | 0.00 | 1.00 | 14.00 | 60.00 | 31.00 | 88.40 | 95.80 |
Table 1: LIBERO-Pro evaluates the success rate of each model across Object, Goal, and Spatial tasks
Figure 4: (a) illustrates the robot performing diverse real-world tasks, including precise insertion, pick-and-place, proprioceptive signals task and long-horizon instruction following. (b) shows the success rate of these tasks.
Figure 5: Zero-shot transfer to LIBERO-Pro Long under different library retention ratios. Success rates are averaged over ten tasks.
| Category | Task Type | Full META-Robot (%) | Without Predicate (%) |
|---|---|---|---|
| Object | Task | 99.00 | 85.00 |
| Pos | 99.60 | 91.00 | |
| Goal | Task | 69.80 | 56.00 |
| Pos | 85.80 | 69.00 | |
| Spatial | Task | 95.80 | 76.00 |
| Pos | 89.25 | 72.00 |
Table 2: Ablation Study of META-Robot across Different Tasks on LIBERO-Pro: We compare the task success rates (%) of full META-Robot and its variant without action-level predicates across the Object, Goal, and Spatial suites under task and position perturbations. Removing predicates consistently reduces performance across all six settings, demonstrating their contribution to execution assessment and targeted repair.
Figure 6: Effect of Proprioceptive Feedback on Manipulation Success: (a) shows the execution process during the simulation, while (b) displays the success rates before and after the ablation of the agent’s signals.
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: We validate the pick-and-place ability of META-Robot on a real-world Galaxea R1 Lite. A yellow tennis ball and an open cardboard box sit on the table; only the right arm moves. The operator names neither a grasp nor a drop point. We find META-Robot can easily complete this task. We find that META-Robot can pick and place from two live object poses rather than from a saved trajectory or large training data.
Figure 8: We validate META-Robot’s long-horizon manipulation capability on a real-world Galaxea R1 Lite. A peach, a tennis ball, an apple, and an open cardboard box sit on the table; only the right arm moves. This task shows the robot’s ability to perform long-horizon pick-and-place tasks.
Figure 9: We validate the order-following ability of Meta-Robot on a real-world Galaxea R1 Lite using three inverted plastic cups of the same size—orange, purple, and green. The focus of this task is the robot’s ability to follow the specified order, rather than the individual grasping and transfer motions. This real-world task evaluates generalization across different specified color orders.
Figure 10: We validate the ability of META-Robot to fold a soft object on a real-world Galaxea R1 Lite using one flat purple cloth on the table. This task requires the use of proprioceptive signals as the towel is very thin and is quite difficult to grasp using visual feedback alone. META-Robot gives the plan: a bimanual first fold that turns the square into a half-width rectangle, then a right-arm second fold that turns that rectangle into a quarter. Both arms move in the first half; only the right arm moves in the second. The first episode makes the half-fold; the second episode makes the quarter. We validate its ability to handle complex tasks.
Figure 11: We validate the breakfast task of META-Robot on a real-world Galaxea R1 Lite. A brown toast slice, a small black Oreo, and a pretzel sit on the table; only one arm moves. We use a fixed two-layer order: Oreo onto the bread, then pretzel on top of that stack. This task is also an evaluation of the system’s use of proprioceptive feedback.
Figure 12: We validate the weighing capability of META-Robot on a real-world Galaxea R1 Lite using three visually identical plastic bottles whose fill levels—empty, half, and full—cannot be recovered from RGB appearance. META-Robot uses its proprioceptive signal to realize this task with high accuracy.
Figure 13: We validate a precision plug-insertion task of META-Robot on a real-world Galaxea R1 Lite. A small three-pin plug and a white power strip are placed at randomized poses on the table. This task shows the use of proprioceptive signal in precise task.
Figure 14: Zero-shot transfer to LIBERO-Pro Long tasks: we evaluate ten representative manipulation tasks under 25%, 50%, 75%, and 100% information retention. The tasks include T01 soup-and-sauce placement, T02 cheese-and-butter placement, T03 stove/moka-pot interaction, T04 bowl-in-drawer placement, T05 mug-to-plate placement, T06 book-to-caddy placement, T07 mug-and-pudding arrangement, T08 soup-and-cheese placement, T09 moka-pot placement, and T10 mug-in-microwave placement. Each radar plot reports per-task success rates, with the mean success rate shown below.
Figure 15: We extend the base model comparison shown in CaP-X Fu et al. [2026] with DeepSeek-V4-Flash and GPT-6 Astra, reporting task success rates of 31% and 45%, respectively.