Claims Browser

Universal physical laws govern spatiotemporal dynamics regardless of the actor

"CLAP is grounded in the insight that universal physical laws govern spatiotemporal dynamics regardless of the actor."

State-of-the-art action-conditioned video models are restricted to single robot embodiments and cannot leverage heterogeneous video data

"State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpus of heterogeneous video data that contains rich signals for learning generalizable physics."

CLAP is capable of being trained on diverse, internet-scale videos across human and robotic agents for cross-embodiment action-conditioned video generation

"we introduce CLAP, a framework for cross-embodiment action-conditioned video generation capable of being trained on diverse, internet-scale videos across human and robotic agents."

CLAP approaches or surpasses state-of-the-art single-embodiment video models in challenging environments like DROID

"Crucially, CLAP approaches or surpasses state-of-the-art single-embodiment video models in challenging environments like DROID."

CLAP establishes a novel paradigm for training single-embodiment video world models through few-shot adaptation

"These performance advantages compound via few-shot adaptation to establish a novel paradigm for training single-embodiment video world models."

CLAP delivers the most comprehensive suite of action-conditioned video world models to date spanning diverse action-conditioning spaces and robot morphologies

"CLAP delivers the most comprehensive suite of action-conditioned video world models to date - spanning diverse action-conditioning spaces (end-effector, language, and latent) and robot morphologies (including cross-embodiment, DROID, Bridge, bimanual YAM robots, and G1 humanoids)."

Multi-modal Large Language Models used for task planning in industrial human-robot collaboration inherently lack an understanding of system states and do not track state transitions, leading to hallucinated actions that deviate from the intended goal

"MM-LLMs inherently lack an understanding of system states and do not track state transitions, often leading to hallucinated actions that deviate from the intended goal"

Generating action plans in natural language limits the plans to a high level and introduces ambiguity in action execution

"generating action plans in natural language tends to limit the generated plans to a high level, introducing ambiguity in action execution"

State-aware Task Estimator and Planner (STEP) outperforms state-of-the-art by 32.8% in action executability and 14.8% in final-state error for robot assembly tasks

"Our approach outperforms the state-of-the-art by 32.8% in action executability and 14.8% in final-state error"

Prompting Multi-modal LLMs to explicitly estimate system state and predict state transitions ensures task-convergent planning in human-robot collaboration

"By forecasting future states alongside actions, STEP ensures task-convergent planning while also providing additional assistance parameters necessary for executing the predicted actions"

World Action Models augment robot policies by predicting how task-relevant scene states may evolve under interaction

"World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction."

Recent World Action Models increasingly perform prediction in latent representation spaces, avoiding full appearance-level generation while preserving control-relevant information

"Recent WAMs increasingly perform such prediction in latent representation spaces, avoiding full appearance-level generation while preserving control-relevant information."

Latent transitions in existing WAMs are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution

"Yet latent transitions are commonly realized with Transformer-based predictors whose inductive structure is centered on token interaction rather than temporal evolution."

LEON improves closed-loop performance and robustness in World Action Models across two formulations while remaining effective under full transition replacement

"Across two WAM formulations that integrate latent prediction into the policy differently, LEON improves closed-loop performance and robustness while remaining effective under full transition replacement."

Transition realization is a consequential architectural choice in latent World Action Models

"These results establish transition realization as a consequential architectural choice in latent WAMs."

PDPO (Planning Diffusion Policy Optimization) achieves an improved success rate over strong baselines in crowd navigation tasks

"Experiments show that PDPO obtains an improved success rate over strong baselines"

Existing reinforcement-learning methods for robot crowd navigation are limited because they output a single reactive action at each timestep, which constrains their ability to represent diverse short-term avoidance strategies

"Existing reinforcement-learning methods typically output a single reactive action at each timestep, which limits their ability to represent diverse short-term avoidance strategies."

Action chunks are especially important for crowd navigation in bounded environments where boundary violations are treated as collisions

"ablations demonstrate that action chunks are especially important for the modified bounded benchmark"