Weiyu Liu

Qineng Wang, Wenlong Huang, Yu Zhou et al.

Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models (VLMs), trained largely in a disembodied manner, exhibit signs of embodied cognition? We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling from egocentric interaction in a visual question answering (VQA) format. Framed as a partially observable Markov decision process (POMDP) whose actions are scene graph changes, ENACT comprises two complementary sequence reordering tasks: forward world modeling (reorder shuffled observations given actions) and inverse world modeling (reorder shuffled actions given observations). While conceptually simple, solving these tasks implicitly demands capabilities central to embodied cognition-affordance recognition, action-effect reasoning, embodied awareness, and interactive, long-horizon memory from partially observable egocentric input, while avoiding low-level image synthesis that could confound the evaluation. We provide a scalable pipeline that synthesizes QA pairs from robotics simulation (BEHAVIOR) and evaluates models on 8,972 QA pairs spanning long-horizon home-scale activities. Experiments reveal a performance gap between frontier VLMs and humans that widens with interaction horizon. Models consistently perform better on the inverse task than the forward one and exhibit anthropocentric biases, including a preference for right-handed actions and degradation when camera intrinsics or viewpoints deviate from human vision. Website at https://enact-embodied-cognition.github.io/.

Chengshu Li, Mengdi Xu, Arpit Bahety et al. · stanford

Imitation learning from large-scale, diverse human demonstrations has proven effective for training robots, but collecting such data is costly and time-consuming. This challenge is amplified for multi-step bimanual mobile manipulation, where humans must teleoperate both a mobile base and two high-degree-of-freedom arms. Prior automated data generation frameworks have addressed static bimanual manipulation by augmenting a few human demonstrations in simulation, but they fall short for mobile settings due to two key challenges: (1) determining base placement to ensure reachability, and (2) positioning the camera to provide sufficient visibility for visuomotor policies. To address these issues, we introduce MoMaGen, which formulates data generation as a constrained optimization problem that enforces hard constraints (e.g., reachability) while balancing soft constraints (e.g., visibility during navigation). This formulation generalizes prior approaches and provides a principled foundation for future methods. We evaluate MoMaGen on four multi-step bimanual mobile manipulation tasks and show that it generates significantly more diverse datasets than existing methods. Leveraging this diversity, MoMaGen can train successful imitation learning policies from a single source demonstration, and these policies can be fine-tuned with as few as 40 real-world demonstrations to achieve deployment on physical robotic hardware. More details are available at our project page: momagen.github.io.