LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation

Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little signal and binary preferences collapse many competing notions of quality into one ambiguous signal. We introduce Freeform Preference Learning (FPL), a method for learning robot policies from freeform human preferences. Rather than asking annotators which of two trajectories is better overall, FPL lets them define natural-language preference axes, such as speed, safety, quality of placement, or carefulness, and provide pairwise preferences along each axis. These annotations are used to learn a language-conditioned reward model that maps a trajectory and preference label to an axis-specific reward. We use this model to train a reward-conditioned policy that optimizes across the multiple human-specified dimensions. Across four real-world and two simulated long-horizon manipulation tasks, FPL improves over sparse-reward and binary-preference methods by 38 percentage points. Beyond improved performance, FPL learns dense progress signals without explicit subtask segmentation, shows compositionality of behavior not present in the data, and allows users to steer the policy towards different behaviors at test time without retraining. Blog post with videos available at https://freeform-pl.github.io/fpl.website/

Natural-language instructions rarely specify every detail required for embodied action. An agent asked to ``prepare an apple,'' for example, must still determine whether to wash or cut it, where to place it, and in what order to perform these actions. Such decisions often reflect user-specific preferences that are demonstrated through behavior but never explicitly stated. We study whether embodied agents can infer these latent preferences from a small number of prior demonstrations and apply them when planning in new situations. To support systematic evaluation, we introduce Preference-based Planning (PbP), a benchmark containing 5,000 evaluation groups and 290 preferences organized into three levels: atomic action parameters, strategic interaction and placement policies, and temporal ordering constraints. We further propose Inferring the Unspoken (InTU), a two-stage framework that first verbalizes the preference inferred from multimodal behavioral demonstrations and then generates an action plan conditioned on that explicit representation. Experiments with video-language and language models reveal a substantial preference-acquisition gap: models plan effectively when given the ground-truth preference, but their performance degrades sharply when the same preference must be inferred from behavior. Explicit verbalization consistently improves alignment over direct end-to-end planning, particularly for strong multimodal models, and provides greater robustness when preferences must transfer across visually distinct scenes. These results identify visual-to-semantic preference acquisition, rather than preference-conditioned planning alone, as a central bottleneck in personalized embodied intelligence. They also demonstrate that language can serve as an interpretable and transferable intermediate representation between observed behavior and personalized action.

General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from digital environments to embodied manipulation. To investigate this question, we introduce LIBERO-Agent, an agent-native benchmark for evaluating these agents in robot manipulation tasks. Rather than asking agents to submit task-level Python control programs or operate through high-level robot skills, LIBERO-Agent provides an interactive robotic environment where agents can select which observations to inspect, process them with their own tools, and issue native action commands. LIBERO-Agent integrates 200 tasks into a common interaction framework and provides a 30-task primary suite that separates perception, short-horizon execution, and long-horizon composition. Results reveal a pronounced reliability gap: while agents perform well on perception and easy short-horizon tasks, their performance degrades substantially on hard short-horizon and long-horizon tasks. Richer observations improve short-horizon manipulation, while demonstration benefits depend on the agent and format. Among these agents, GPT-6 Astra achieves the strongest overall performance. Further analysis shows its major advantage lies in mechanism interaction, especially when sustained physical contact is needed, while its remaining failures stem from cross-stage interference and geometric errors.