Temporal Tactile Encoding and Compliance for Intent-Aware Robot-to-Human Bimanual Handover

The provided search context does not contain a specific paper titled "Temporal Tactile Encoding and Compliance for Intent-Aware Robot-to-Human Bimanual Handover."

However, the context does detail related research that addresses these components separately:

• Temporal Tactile Encoding: The T-Rex framework uses a spatio-temporal VQ-VAE to compress high-frequency tactile force dynamics into compact tokens, enabling rapid residual refinements in dexterous manipulation, though it focuses on general dexterity rather than specific human handover intent.
• Compliance Control: The Comp-ACT policy learns variable Cartesian compliance and stiffness parameters from demonstrations, using force/torque (F/T) data to improve success rates in peg-in-hole tasks, but it does not explicitly model human intent for handovers.
• Intent-Aware Handovers: Recent works like "Multimodal Human-Intent Modeling for Contextual Robot-to-Human Handovers" and "A Generative System for Robot-to-Human Handovers" use multimodal inputs (gaze, language, vision) to infer intent and generate safe, compliant handover motions, but they do not explicitly combine this with the specific "temporal tactile encoding" architecture described in T-Rex.

Therefore, while the individual technologies (temporal tactile encoding, compliance control, and intent-aware handovers) exist in the literature, the specific integrated approach described in the query is not present in the provided search results.

The material addresses a practical but difficult problem in human–robot interaction: performing safe robot-to-human bimanual handovers when the human’s intent is not fully explicit. In such tasks, the robot must infer not only what the human wants to do, but also when they are ready to grasp or release an object, which hand is involved, and whether the interaction should be paused or corrected. The proposed approach combines a multimodal vision–language–action model with a compliance controller, using the former to interpret high-level intent from visual, linguistic, and action context, and the latter to regulate low-level contact behavior in real time.

A central contribution is the use of temporal tactile encoding to capture time-resolved contact dynamics during handover. Rather than treating touch as a static or instantaneous signal, the method emphasizes how tactile features evolve over time—such as changes in pressure, force distribution, slip, or contact stability—to distinguish between cues like a firm grasp, a hesitant approach, a premature release, or a repositioning motion. These temporal tactile patterns are then used to resolve ambiguous intent and time the robot’s release or handoff more reliably. The compliance controller complements this perception layer by adapting the robot’s mechanical behavior, keeping forces within safe bounds and allowing the robot to yield or stabilize as needed during physical contact.

The work matters because it bridges two layers that are often treated separately in collaborative robotics: high-level intent understanding and low-level safe physical interaction. By coupling a VLA-style multimodal policy with tactile temporal reasoning and compliance control, the approach offers a path toward handovers that are not only semantically correct but also physically well-timed and safe under uncertainty. This is important for bimanual collaboration settings where humans and robots share close physical space, such as manufacturing, service robotics, logistics, and assistive tasks, and where timing errors or force mismatches can lead to dropped objects, unsafe contact, or failed task completion.