Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video

Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception errors and embodiment gap. To address this, we introduce Video2Sim2Real, a full-stack framework for autonomous skill acquisition from a single human manipulation video. Our framework first uses off-the-shelf foundation models to reconstruct a simulator-ready digital twin and extract robot and object motion priors. Rather than treating the extracted robot motion as a reliable reference throughout execution, our key idea is to recover and leverage the most fundamental sources of supervision from the demonstrated skill: We identify object-centric keyframes to optimize the corresponding robot configurations using object information from the simulator, and use these configurations as anchors that refine the robot motion such that it ultimately has the desired impact on the environment. To bridge the remaining sim-to-real gap, we introduce a sim-to-real strategy that decouples robustness to noisy and incomplete perception from variations in hand-object interaction dynamics. Specifically, we learn to recalibrate robot configurations from noisy real-world point clouds via IL, and leverage residual RL to perform local finger-level adaptations to ensure for robust and effective interactions. Finally, a collision-aware motion planning module enables spatial generalization to novel object configurations. Across several everyday manipulation tasks, Video2Sim2Real improves simulated task success, safety, and trajectory coherence over numerous baselines, and achieves better sim-to-real transfer than existing techniques. These results demonstrate a promising path toward autonomous dexterous skill acquisition from human videos.

Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory

AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet existing methods (RAG and Graph-RAG) primarily retrieve memory through similarity-based mechanisms. While efficient, such System-1-style retrieval struggles with scenarios that require global reasoning or comprehensive coverage of all relevant information. In this work, We propose Mnemis, a novel memory framework that integrates System-1 similarity search with a complementary System-2 mechanism, termed Global Selection. Mnemis organizes memory into a base graph for similarity retrieval and a hierarchical graph that enables top-down, deliberate traversal over semantic hierarchies. By combining the complementary strength from both retrieval routes, Mnemis retrieves memory items that are both semantically and structurally relevant. Mnemis achieves state-of-the-art performance across all compared methods on long-term memory benchmarks, scoring 93.9 on LoCoMo and 91.6 on LongMemEval-S using GPT-4.1-mini.

Going with the Flow: Koopman Behavioral Models as Implicit Planners for Visuo-Motor Dexterity

There has been rapid and dramatic progress in robots' ability to learn complex visuo-motor manipulation skills from demonstrations, thanks in part to expressive policy classes that employ diffusion- and transformer-based backbones. However, these design choices require significant data and computational resources and remain far from reliable, particularly within the context of multi-fingered dexterous manipulation. Fundamentally, they model skills as reactive mappings and rely on fixed-horizon action chunking to mitigate jitter, creating a rigid trade-off between temporal coherence and reactivity. In this work, we introduce Unified Behavioral Models (UBMs), a framework that learns to represent dexterous skills as coupled dynamical systems that capture how visual features of the environment (visual flow) and proprioceptive states of the robot (action flow) co-evolve. By capturing such behavioral dynamics, UBMs can ensure temporal coherence by construction rather than by heuristic averaging. To operationalize these models, we propose Koopman-UBM, a first instantiation of UBMs that leverages Koopman Operator theory to effectively learn a unified representation in which the joint flow of latent visual and proprioceptive features is governed by a structured linear system. We demonstrate that Koopman-UBM can be viewed as an implicit planner: given an initial condition, it analytically computes the desired robot behavior while simultaneously ''imagining'' the resulting flow of visual features over the entire skill horizon. To enable reactivity and adaptation, we introduce an online replanning strategy in which the model acts as its own runtime monitor that automatically triggers replanning when predicted and observed visual flow diverge beyond a threshold. Across seven simulated tasks and two real-world tasks, we demonstrate that K-UBM matches or exceeds the performance of state-of-the-art baselines, while offering considerably faster inference, smooth execution, robustness to occlusions, and flexible replanning.