ContactHandover: Contact-Guided Robot-to-Human Object Handover
Not on the map. Why?
Abstract (arXiv)
Robot-to-human object handover is an important step in many human robot collaboration tasks. A successful handover requires the robot to maintain a stable grasp on the object while making sure the human receives the object in a natural and easy-to-use manner. We propose ContactHandover, a robot to human handover system that consists of two phases: a contact-guided grasping phase and an object delivery phase. During the grasping phase, ContactHandover predicts both 6-DoF robot grasp poses and a 3D affordance map of human contact points on the object. The robot grasp poses are re-ranked by penalizing those that block human contact points, and the robot executes the highest ranking grasp. During the delivery phase, the robot end effector pose is computed by maximizing human contact points close to the human while minimizing the human arm joint torques and displacements. We evaluate our system on 27 diverse household objects and show that our system achieves better visibility and reachability of human contacts to the receiver compared to several baselines. More results can be found on https://clairezixiwang.github.io/ContactHandover.github.io
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 1 Apr 2024.
Builds on
20 of 43 references.
References · 20
- 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 333
- 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
- 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 237
- 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 213
- 2021Hand-Object Contact Consistency Reasoning for Human Grasps Generationcited by 160
- 2019ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imagingcited by 145
- 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 124
- 2023Affordance Diffusion: Synthesizing Hand-Object Interactionscited by 110
- 20213D AffordanceNet: A Benchmark for Visual Object Affordance Understandingcited by 100
- 2019ContactGrasp: Functional Multi-finger Grasp Synthesis from Contactcited by 87
- 2023HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Videocited by 76
- 2022End-to-End Affordance Learning for Robotic Manipulationcited by 54
8 more
- 2023One-Shot Open Affordance Learning with Foundation Modelscited by 45
- 2020Object Handovers: a Review for Roboticscited by 28
- 2019Learning Grasp Affordance Reasoning through Semantic Relationscited by 22
- 2021H2O: A Benchmark for Visual Human-human Object Handover Analysiscited by 16
- 2020Affordance-Aware Handovers with Human Arm Mobility Constraintscited by 8
- 2018Workspace Aware Online Grasp Planningcited by 4
- 2023Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordancecited by 4
- 2022BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environmentcited by 3
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Point2Act: Efficient 3D Distillation of Multimodal LLMs for Zero-Shot Context-Aware Graspingcited by 2
- 2025Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handoverscited by 0
- 2026OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interactioncited by 0
- 2026OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interaction30 Apr 2026
- 2025Point2Act: Efficient 3D Distillation of Multimodal LLMs for Zero-Shot Context-Aware Grasping5 Aug 2025
- 2025Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers5 Mar 2025