STEPS: Scene-Grounded Task Escalation through Progressive Skill Learning toward Open-Ended Goals

Code-as-Policy robot agents increasingly improve through practice, yet evalua- tion on predefined tasks makes it difficult to distinguish genuine capability acqui- sition from adaptation to practiced instances. We present STEPS, a framework for evaluating self-improvement on human-specified goals such as “tidy the office desk”. STEPS generates physically valid, oracle-solvable scenes; defines the full goal as an L5 task and derives L1-L4 diagnostic tasks probing subtasks, compo- nent capabilities, and constraints; and evaluates a frozen agent on the practiced L5 task, pose-perturbed L5 instances, and held-out lower-level tasks. Across five goals and 105 tasks, a skill library developed with human assistance achieves over 90% success on practiced L5 tasks but performs substantially worse on regener- ated instances and held-out L3-L4 tasks. On one evolution route, skills distilled from L1-L4 practice usually pass isolated validation but are rarely invoked when solving the full goal. Despite this fitting, some learned capabilities transfer be- yond the development scenes to external manipulation tasks, and weaknesses ex- posed by STEPS provide actionable targets for capability updates that further im- prove cross-domain performance. These results show that practiced-task success alone can overstate capability acquisition and should be complemented with goal- derived diagnostics that reveal what transfers, where improvement breaks down, and what capability to target next.