InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation
InterEvolve evolves reward programs at test time so humanoid controllers solve new loco-manipulation tasks without retraining.
InterEvolve adapts a frozen humanoid controller to loco-manipulation tasks it was never trained for by evolving reward programs at test time. An object-aware forward-backward model converts body and object rewards into behavior, while an LLM revises staged programs and a numerical optimizer tunes constants using parallel simulation feedback. Evolved programs uncover strategies missed by human-designed rewards and run autonomously on a physical Unitree G1 using egocentric onboard perception.
- Test-time reward evolution avoids retraining the controller
- An LLM revises staged reward programs from execution feedback
- Evolved skills run on a physical Unitree G1
- Human-designed rewards left much controller competence unused
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We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining. Our key insight is that a broad controller already holds much of the competence a new task needs, and that this competence becomes accessible through an interface between planning and control that is expressive enough to specify contact-rich, multi-stage interactions, yet executable and measurable enough that execution feedback can guide planning from experience. InterEvolve realizes this interface with two components. First, we develop an object-aware forward-backward (FB) behavioral foundation model, whose object residuals on a frozen body prior turn a new reward about the body or objects into loco-manipulation behavior at test time. Second, we specify tasks as reward programs: staged rewards with completion conditions and tunable constants. A large language model (LLM) agent revises the program structure in context, drawing on execution feedback and a skill library of verified programs, while a numerical optimizer tunes its constants. With every candidate verified across parallel simulation scenarios, the program explores new ways to induce, repurpose, and compose the controller's existing motor competence for the task at hand, and thus improves over iterations. Experiments show that human-designed rewards leave much of the FB model's loco-manipulation competence untapped, whereas the programs InterEvolve evolves release it, sometimes through novel strategies. It further produces behaviors for diverse tasks, complex scenes, and long-horizon compositions in simulation, and evolved skills run autonomously on a physical Unitree G1 from egocentric onboard perception.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2610.02196