For the egocentric HOI, in addition to perceiving semantics e.g., "what" interaction is occurring, capturing "where" the interaction specifically manifests in 3D space is

Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories. Few-shot learning is a promising solution for alleviating this issue by allowing robots to perform a few interactions with unseen objects.

Inreinforcement learning, option models (Sutton, Precup & Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in agiven situation, sometimes termed affordable actions.

Understanding egocentric human-object interaction (HOI) is a fundamental aspect of human-centric perception, facilitating applications like AR/VR and embodied AI. For the egocentric HOI, in addition to perceiving semantics e.g., ''what'' interaction is occurring, capturing ''where'' the interaction specifically manifests in 3D space is also crucial, which links the perception and operation. Existing methods primarily leverage observations of HOI to capture interaction regions from an exocentric view. However, incomplete observations of interacting parties in the egocentric view introduce ambiguity between visual observations and interaction contents, impairing their efficacy. From the egocentric view, humans integrate the visual cortex, cerebellum, and brain to internalize their intentions and interaction concepts of objects, allowing for the pre-formulation of interactions and making behaviors even when interaction regions are out of sight.

Zero-Shot Object Goal Navigation (ZS-OGN) enables robots to navigate toward objects of unseen categories without prior training. Traditional approaches often leverage categorical semantic information for navigation guidance, which struggles when only partial objects are observed or detailed and functional representations of the environment are lacking. To resolve the above two issues, we propose \textit{Geometric-part and Affordance Maps} (GAMap), a novel method that integrates object parts and affordance attributes for navigation guidance. Our method includes a multi-scale scoring approach to capture geometric-part and affordance attributes of objects at different scales. Comprehensive experiments conducted on the HM3D and Gibson benchmark datasets demonstrate improvements in Success Rates and Success weighted by Path Length, underscoring the efficacy of our geometric-part and affordance-guided navigation approach in enhancing robot autonomy and versatility, without any additional task-specific training or fine-tuning with the semantics of unseen objects and/or the locomotions of the robot.

Embodied agents operating in open environments must translate high-level instructions into grounded, executable behaviors, often requiring coordinated use of both hands. While recent foundation models offer strong semantic reasoning, existing robotic task planners remain predominantly unimanual and fail to address the spatial, geometric, and coordination challenges inherent to bimanual manipulation in scene-agnostic settings. We present a unified framework for scene-agnostic bimanual task planning that bridges high-level reasoning with 3D-grounded two-handed execution. Our approach integrates three key modules. Visual Point Grounding (VPG) analyzes a single scene image to detect relevant objects and generate world-aligned interaction points. Bimanual Subgoal Planner (BSP) reasons over spatial adjacency and cross-object accessibility to produce compact, motion-neutralized subgoals that exploit opportunities for coordinated two-handed actions. Interaction-Point-Driven Bimanual Prompting (IPBP) binds these subgoals to a structured skill library, instantiating synchronized unimanual or bimanual action sequences that satisfy hand-state and affordance constraints. Together, these modules enable agents to plan semantically meaningful, physically feasible, and parallelizable two-handed behaviors in cluttered, previously unseen scenes. Experiments show that it produces coherent, feasible, and compact two-handed plans, and generalizes to cluttered scenes without retraining, demonstrating robust scene-agnostic affordance reasoning for bimanual tasks.