Cross-Embodiment Affordance Transfer for Robotic Manipulation
Radboud University（拉德堡德大学）；School of Artificial Intelligence（人工智能学院）；Donders Centre for Cognition（东德斯认知中心）
Sawera Yaseen, Serge Thill, Rekha Raja
备注：
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Robotic manipulation systems commonly learn object affordances from human-annotated datasets, which reflect interaction possibilities for a human hand rather than the physical constraints of a specific robot. For example, a surface region of an object identified as graspable by a human annotator may be geometrically unreachable or mechanically infeasible for a robot, yet this mismatch is rarely addressed explicitly. To tackle this challenge, we proposed a framework that translates human affordance annotations into robot-executable affordance predictions conditioned on the physical properties of the robot. The network predicts which surface regions of an object are physically executable by the robot, given a 3D point cloud of an object and a descriptor encoding the robot's geometry and constraints. The model was trained using human-annotated affordances together with robotic execution outcomes from multiple sampled approach trials in simulation, where execution outcomes determined which regions are executable for the robot. The evaluation was performed on a household objects dataset using two parallel-jaw gripper configurations on a UR5e robot arm. The framework outperformed a model that does not receive information about the robot. The experimental results demonstrated that affordance prediction must be conditioned on the robot and that human annotations alone are insufficient for learning robot-executable affordance representations.
University of Liverpool（利物浦大学）；King’s College London（伦敦国王学院）；Monash University（莫纳什大学）
Transparent objects are widely used in our daily lives and therefore robots need to be able to handle them. However, transparent objects suffer from light reflection and refraction, which makes it challenging to obtain the accurate depth maps required to perform handling tasks. In this paper, we propose a novel affordance-based framework for depth reconstruction and manipulation of transparent objects, named A4T. A hierarchical AffordanceNet is first used to detect the transparent objects and their associated affordances that encode the relative positions of an object's different parts. Then, …
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
TAMS (Technical Aspects of Multimodal Systems), Department of Informatics, Universität Hamburg（技术多模态系统（TAMS），信息学院，汉堡大学）；Agile Robots AG（敏捷机器人公司）；Technical University of Munich（慕尼黑技术大学）
3D Affordance Keypoint Detection for Robotic Manipulation
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）
Cross-Embodiment Robot Manipulation via a Unified Hand Action Space
Intelligent Robotics and Vision Lab, University of Texas at Dallas（德克萨斯大学达拉斯分校智能机器人与视觉实验室）；Intelligent Robotics and Interactive Systems Lab, Arizona State University（亚利桑那州立大学智能机器人与交互系统实验室）
A*STAR Institute for Infocomm Research (IR)（A*STAR资讯通信研究院（IR））；A*STAR Institute of High Performance Computing (IHPC)（A*STAR高性能计算研究院（IHPC））
Robots are increasingly expected to manipulate objects in ever more unstructured environments where the object properties have high perceptual uncertainty from any single sensory modality. This directly impacts successful object manipulation. In this work, we propose a reinforcement learning-based motion planning framework for object manipulation which makes use of both on-the-fly multisensory feedback and a learned attention-guided deep affordance model as perceptual states. The affordance model is learned from multiple sensory modalities, including vision and touch (tactile and force/torque)…
RoboAfford++: A Generative AI-Enhanced Dataset for Multimodal Affordance Learning in Robotic Manipulation and Navigation
Xiaomi EV（小米电动车）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）
IEETA - University of Aveiro（阿威罗大学电子与电信研究所）；University of Groningen（格罗宁根大学）
Service robots are expected to autonomously and efficiently work in human-centric environments. For this type of robots, object perception and manipulation are challenging tasks due to need for accurate and real-time response. This paper presents an interactive open-ended learning approach to recognize multiple objects and their grasp affordances concurrently. This is an important contribution in the field of service robots since no matter how extensive the training data used for batch learning, a robot might always be confronted with an unknown object when operating in human-centric environme…
Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning
Department of Computer Science, Technical University of Darmstadt（达姆施塔特工业大学计算机科学系）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, Technical University of Darmstadt（达姆施塔特工业大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt（黑森州人工智能中心）；Robotics Institute Germany (RIG)（德国机器人研究所）
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology（香港科技大学）；National University of Singapore（新加坡国立大学）；Rutgers University（罗格斯大学）；Hefei University of Technology（合肥工业大学）；Shanghai AI Lab（上海人工智能实验室）
EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
Faculty of Robot Science and Engineering, Northeastern University（机器人科学与工程学院，东北大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；School of Automation, Nanjing University of Information Science and Technology（自动化学院，南京信息科学技术大学）
Affordance information about a scene provides important clues as to what actions may be executed in pursuit of meeting a specified goal state. Thus, integrating affordance-based reasoning into symbolic action plannning pipelines would enhance the flexibility of robot manipulation. Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation. Object priors limit generalization to unknown object categories. This paper describes an affordance recognition pipeline based on a category-agnostic region propo…
University of Michigan（密歇根大学）；Robotics Institute（机器人研究所）
In order to enable robust operation in unstructured environments, robots should be able to generalize manipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, an…
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
University of Pennsylvania（宾夕法尼亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Nanyang Technological University（南洋理工大学）；University of Oxford（牛津大学）；Massachusetts Institute of Technology（麻省理工学院）
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can generalize over objects with different semantic categories, diverse shape geometry and versatile functionality. Recently, the technique of visual affordance has shown great prospects in providing object-centric information priors with effective actionable semantics. As such, an effective policy can be trained to open a door by knowing how to exert force on the handle. However, to learn the affordance, it often requir…
CSAIL, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
We would like robots to achieve purposeful manipulation by placing any instance from a category of objects into a desired set of goal states. Existing manipulation pipelines typically specify the desired configuration as a target 6-DOF pose and rely on explicitly estimating the pose of the manipulated objects. However, representing an object with a parameterized transformation defined on a fixed template cannot capture large intra-category shape variation, and specifying a target pose at a category level can be physically infeasible or fail to accomplish the task -- e.g. knowing the pose and s…
AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation
The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Knowin AI（考拉悠然科技）
The Hong Kong Polytechnic University（香港理工大学）；Technical University of Denmark（丹麦技术大学）
While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepanci…
AM-Bench: A Modular Simulation Suite and Benchmark for Aerial Manipulation Policy Learning
The Robotics Institute, School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机学院机器人研究所）；Department of Aerospace Engineering, Pennsylvania State University（宾夕法尼亚州立大学航空航天工程系）；School of Mechanical Engineering, Kyung Hee University（庆熙大学机械工程学院）
