Author name cluster

Kaiyu Hang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

25 papers

1 author row

IROS Conference 2025 Conference Paper

ARC-Calib: Autonomous Markerless Camera-to-Robot Calibration via Exploratory Robot Motions

Podshara Chanrungmaneekul
Yiting Chen
Joshua T. Grace
Aaron M. Dollar
Kaiyu Hang

Camera-to-robot (also known as eye-to-hand) calibration is a critical component of vision-based robot manipulation. Traditional marker-based methods often require human intervention for system setup. Furthermore, existing autonomous markerless calibration methods typically rely on pre-trained robot tracking models that impede their application on edge devices and require fine-tuning for novel robot embodiments. To address these limitations, this paper proposes a model-based markerless camera-to-robot calibration framework, ARC-Calib, that is fully autonomous and generalizable across diverse robots and scenarios without requiring extensive data collection or learning. First, exploratory robot motions are introduced to generate easily trackable trajectory-based visual patterns in the camera’s image frames. Then, a geometric optimization framework is proposed to exploit the coplanarity and collinearity constraints from the observed motions to iteratively refine the estimated calibration result. Our approach eliminates the need for extra effort in either environmental marker setup or data collection and model training, rendering it highly adaptable across a wide range of real-world autonomous systems. Extensive experiments are conducted in both simulation and the real world to validate its robustness and generalizability.

IROS Conference 2025 Conference Paper

B4P: Simultaneous Grasp and Motion Planning for Object Placement via Parallelized Bidirectional Forests and Path Repair

Benjamin H. Leebron
Kejia Ren
Yiting Chen
Kaiyu Hang

Robot pick and place systems have traditionally decoupled grasp, placement, and motion planning to build sequential optimization pipelines with an assumption that the individual components will be able to work together. However, this separation introduces sub-optimality, as grasp choices may limit, or even prohibit, feasible motions for a robot to reach the target placement pose, particularly in cluttered environments with narrow passages. To this end, we propose a forest-based planning framework to simultaneously find grasp configurations and feasible robot motions that explicitly satisfy downstream placement configurations paired with the selected grasps. Our proposed framework leverages a bidirectional sampling-based approach to build a start forest, rooted at the feasible grasp regions, and a goal forest, rooted at the feasible placement regions, to facilitate the search through randomly explored motions that connect valid pairs of grasp and placement trees. We demonstrate that the framework’s inherent parallelism enables superlinear speedup, making it scalable for applications for redundant robot arms, e. g. , 7 DoF, to work efficiently in highly cluttered environments. Extensive experiments in simulation demonstrate the robustness and efficiency of the proposed framework in comparison with multiple baselines under diverse scenarios.

IROS Conference 2025 Conference Paper

On the Role of Jacobians in Robust Manipulation

Joshua T. Grace
Podshara Chanrungmaneekul
Kaiyu Hang
Aaron M. Dollar

Traditional robot control relies on analytical methods that require precise system models, which are hard to apply in real-world settings and limit generalization to arbitrary tasks. However, systems like serial manipulators and passively adaptive hands feature inherently stable regions without control discontinuities like loss of contact or singularities. In these regions, approximate controllers focusing on the correct direction of motion enable successful coarse manipulation. When coupled with a rough estimation of the motion magnitude, precision manipulation is achieved. Leveraging this insight, we introduce a novel inverse Jacobian estimation method that independently estimates the primary motion direction and magnitude of the manipulator’s actuators. Our method efficiently estimates the direct mapping from task to actuator space with no need for a priori system knowledge enabling the same framework to control both hands and arms without compromising task performance. We present a novel control method with no a priori knowledge for precision manipulation. Experiments on the Yale Model O hand, Yale Stewart Hand, and a UR5e arm demonstrate that the inverse Jacobians estimated via our approach enable real-time control with submillimeter precision in manipulation tasks. These results highlight that online self-ID data alone is sufficient for precise real-world manipulation.

IROS Conference 2025 Conference Paper

rt-RISeg: Real-Time Model-Free Robot Interactive Segmentation for Active Instance-Level Object Understanding

Howard H. Qian
Yiting Chen
Gaotian Wang
Podshara Chanrungmaneekul
Kaiyu Hang

Successful execution of dexterous robotic manipulation tasks in new environments, such as grasping, depends on the ability to proficiently segment unseen objects from the background and other objects. Previous works in unseen object instance segmentation (UOIS) train models on large-scale datasets, which often leads to overfitting on static visual features. This dependency results in poor generalization performance when confronted with out-of-distribution scenarios. To address this limitation, we rethink the task of UOIS based on the principle that vision is inherently interactive and occurs over time. We propose a novel real-time interactive perception framework, rt-RISeg, that continuously segments unseen objects by robot interactions and analysis of a designed body frame-invariant feature (BFIF). We demonstrate that the relative rotational and linear velocities of randomly sampled body frames, resulting from selected robot interactions, can be used to identify objects without any learned segmentation model. This fully self-contained segmentation pipeline generates and updates object segmentation masks throughout each robot interaction without the need to wait for an action to finish. We showcase the effectiveness of our proposed interactive perception method by achieving an average object segmentation accuracy rate 27. 5% greater than state-of-the-art UOIS methods. Furthermore, although rt-RISeg is a standalone framework, we show that the autonomously generated segmentation masks can be used as prompts to vision foundation models for significantly improved performance.

IROS Conference 2025 Conference Paper

Wearable Roller Rings to Augment In-Hand Manipulation through Active Surfaces

Hayden Webb
Podshara Chanrungmaneekul
Shenli Yuan
Kaiyu Hang

In-hand manipulation is a crucial ability for reorienting and repositioning objects within grasps. The main challenges in this are not only the complexity of the computational models, but also the risks of grasp instability caused by active finger motions, such as rolling, sliding, breaking, and remaking contacts. This paper presents the development of the Roller Ring (RR), a modular robotic attachment with active surfaces that is wearable by both robot and human hands to manipulate without lifting a finger. By installing the angled RRs on hands, such that their spatial motions are not colinear, we derive a general differential motion model for manipulating objects. Our motion model shows that complete in-hand manipulation skill sets can be provided by as few as only 2 RRs through non-holonomic object motions, while more RRs can enable enhanced manipulation dexterity with fewer motion constraints. Through extensive experiments, we test the RRs on both a robot hand and a human hand to evaluate their manipulation capabilities. We show that the RRs can be employed to manipulate arbitrary object shapes to provide dexterous in-hand manipulation.

ICRA Conference 2024 Conference Paper

Direct Self-Identification of Inverse Jacobians for Dexterous Manipulation Through Particle Filtering

Joshua T. Grace
Podshara Chanrungmaneekul
Kaiyu Hang
Aaron M. Dollar

The ability to plan and control robotic in-hand manipulation is challenged by several issues, including the required amount of prior knowledge of the system and the sophisticated physics that varies across different robot hands or even grasp instances. One of the most direct models of in-hand manipulation is the inverse Jacobian, which can directly map from the desired in-hand object motions to the required hand actuator controls. However, acquiring such inverse Jacobians without complex hand-object system models is typically infeasible. We present a method for controlling in-hand manipulation using inverse Jacobians that are self-identified by a particle filter-based estimation scheme that leverages the ability of underactuated hands to maintain a passively stable grasp during self-identification movements. This method requires no a priori knowledge of the specific hand-object system and learns the system’s inverse Jacobian through small exploratory motions. Our system approximates the underlying inverse Jacobian closely, which can be used to perform manipulation tasks across a range of objects successfully. With extensive experiments on a Yale Model O hand, we show that the proposed system can provide accurate in-hand manipulation of sub-millimeter precision and that the inverse Jacobian-based controller can support real-time manipulation control of up to 900Hz.

IROS Conference 2024 Conference Paper

Interactive Robot-Environment Self-Calibration via Compliant Exploratory Actions

Podshara Chanrungmaneekul
Kejia Ren
Joshua T. Grace
Aaron M. Dollar
Kaiyu Hang

Calibrating robots into their workspaces is crucial for manipulation tasks. Existing calibration techniques often rely on sensors external to the robot (cameras, laser scanners, etc.) or specialized tools. This reliance complicates the calibration process and increases the costs and time requirements. Furthermore, the associated setup and measurement procedures require significant human intervention, which makes them more challenging to operate. Using the built-in force-torque sensors, which are nowadays a default component in collaborative robots, this work proposes a self-calibration framework where robot-environmental spatial relations are automatically estimated through compliant exploratory actions by the robot itself. The self-calibration approach converges, verifies its own accuracy, and terminates upon completion, autonomously purely through interactive exploration of the environment’s geometries. Extensive experiments validate the effectiveness of our self-calibration approach in accurately establishing the robot-environment spatial relationships without the need for additional sensing equipment or any human intervention.

ICRA Conference 2024 Conference Paper

RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features

Howard H. Qian
Yangxiao Lu
Kejia Ren
Gaotian Wang
Ninad Khargonkar
Yu Xiang 0001
Kaiyu Hang

In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other objects. Previous works perform unseen object instance segmentation (UOIS) by training deep neural networks on large-scale data to learn RGB/RGB-D feature embeddings, where cluttered environments often result in inaccurate segmentations. We build upon these methods and introduce a novel approach to correct inaccurate segmentation, such as under-segmentation, of static image-based UOIS masks by using robot interaction and a designed body frame-invariant feature. We demonstrate that the relative linear and rotational velocities of frames randomly attached to rigid bodies due to robot interactions can be used to identify objects and accumulate corrected object-level segmentation masks. By introducing motion to regions of segmentation uncertainty, we are able to drastically improve segmentation accuracy in an uncertainty-driven manner with minimal, non-disruptive interactions (ca. 2-3 per scene). We demonstrate the effectiveness of our proposed interactive perception pipeline in accurately segmenting cluttered scenes by achieving an average object segmentation accuracy rate of 80. 7%, an increase of 28. 2% when compared with other state-of-the-art UOIS methods.

IROS Conference 2024 Conference Paper

UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Control

Gaotian Wang
Kejia Ren
Kaiyu Hang

Nonprehensile manipulation through precise pushing is an essential skill that has been commonly challenged by perception and physical uncertainties, such as those associated with contacts, object geometries, and physical properties. For this, we propose a unified framework that jointly addresses system modeling, action generation, and control. While most existing approaches either heavily rely on a priori system information for analytic modeling, or leverage a large dataset to learn dynamic models, our framework approximates a system transition function via non-parametric learning only using a small number of exploratory actions (ca. 10). The approximated function is then integrated with model predictive control to provide precise pushing manipulation. Furthermore, we show that the approximated system transition functions can be robustly transferred across novel objects while being online updated to continuously improve the manipulation accuracy. Through extensive experiments on a real robot platform with a set of novel objects and comparing against a state-of-the-art baseline, we show that the proposed unified framework is a light-weight and highly effective approach to enable precise pushing manipulation all by itself. Our evaluation results illustrate that the system can robustly ensure millimeter-level precision and can straightforwardly work on any novel object.

ICRA Conference 2023 Conference Paper

Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation

Kejia Ren
Podshara Chanrungmaneekul
Lydia E. Kavraki
Kaiyu Hang

Rearrangement-based nonprehensile manipulation still remains as a challenging problem due to the high-dimensional problem space and the complex physical uncertainties it entails. We formulate this class of problems as a coupled problem of local rearrangement and global action optimization by incorporating free-space transit motions between constrained rearranging actions. We propose a forest-based kinodynamic planning framework to concurrently search in multiple problem regions, so as to enable global exploration of the most task-relevant subspaces, while facilitating effective switches between local rearranging actions. By interleaving dynamic horizon planning and action execution, our framework can adaptively handle real-world uncertainties. With extensive experiments, we show that our framework significantly improves the planning efficiency and manipulation effectiveness while being robust against various uncertainties.

IROS Conference 2023 Conference Paper

Non-Parametric Self-Identification and Model Predictive Control of Dexterous In-Hand Manipulation

Podshara Chanrungmaneekul
Kejia Ren
Joshua T. Grace
Aaron M. Dollar
Kaiyu Hang

Building hand-object models for dexterous in-hand manipulation remains a crucial and open problem. Major challenges include the difficulty of obtaining the geometric and dynamical models of the hand, object, and time-varying contacts, as well as the inevitable physical and perception uncertainties. Instead of building accurate models to map between the actuation inputs and the object motions, this work proposes to enable the hand-object systems to continuously approximate their local models via a self-identification process where an underlying manipulation model is estimated through a small number of exploratory actions and non-parametric learning. With a very small number of data points, as opposed to most data-driven methods, our system self-identifies the underlying manipulation models online through exploratory actions and non-parametric learning. By integrating the self-identified hand-object model into a model predictive control framework, the proposed system closes the control loop to provide high accuracy in-hand manipulation. Furthermore, the proposed self-identification is able to adaptively trigger online updates through additional exploratory actions, as soon as the self-identified local models render large discrepancies against the observed manipulation outcomes. We implemented the proposed approach on a sensorless underactuated Yale Model O hand with a single external camera to observe the object's motion. With extensive experiments, we show that the proposed self-identification approach can enable accurate and robust dexterous manipulation without requiring an accurate system model nor a large amount of data for offline training.

IROS Conference 2022 Conference Paper

Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons

Kejia Ren
Lydia E. Kavraki
Kaiyu Hang

Robot manipulation in cluttered environments of-ten requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target objects. Due to the sophisticated physical interactions involved in such scenarios, rearrangement-based manipulation is still limited to a small range of tasks and is especially vulnerable to physical uncertainties and perception noise. This paper presents a planning framework that leverages the efficiency of sampling-based planning approaches, and closes the manipulation loop by dynamically controlling the planning horizon. Our approach interleaves planning and execution to progressively approach the manipulation goal while correcting any errors or path deviations along the process. Meanwhile, our framework allows the definition of manipulation goals without requiring explicit goal configurations, enabling the robot to flexibly interact with all objects to facilitate the manipulation of the target ones. With extensive experiments both in simulation and on a real robot, we evaluate our framework on three manipulation tasks in cluttered environments: grasping, relocating, and sorting. In comparison with two baseline approaches, we show that our framework can significantly improve planning efficiency, robustness against physical uncertainties, and task success rate under limited time budgets.

IROS Conference 2020 Conference Paper

Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting

Haoran Song
Joshua A. Haustein
Weihao Yuan 0001
Kaiyu Hang
Michael Yu Wang
Danica Kragic
Johannes A. Stork

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable of reliably sorting large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.

IROS Conference 2019 Conference Paper

A Data-Driven Framework for Learning Dexterous Manipulation of Unknown Objects

Andrew S. Morgan
Kaiyu Hang
Walter G. Bircher
Aaron M. Dollar

We address the problem of developing precision, quasi-static control strategies for fingertip manipulation in robot hands. In general, analytically specifying useful object transition maps, or hand-object Jacobians, for scenarios in which there is uncertainty in some key aspect of the hand-object system is difficult or impossible. This could be in scenarios with standard fully-actuated hands where, for instance, there is not an accurate model of the contact conditions, or in scenarios with fewer control inputs than mechanical degrees of freedom (such as underactuated hands or those that are controlled by synergies or impedance controlled frameworks), since the output space is of higher dimension than the input space. In this work, we develop a method for extracting object transition maps by tracking the state of the grasp frame. We begin by modeling a compliant, underactuated hand and its mechanical properties through an energy-based approach. From this energy model, we provide controlled actuation inputs to change the state of the grasp frame. We observe the response from these actions and develop a regression map of the action-reaction pairs, where the map is subject to our intent for grasp frame movement and the regional relationship between the contacts. Once the regression model is developed, we perform within-hand planning of the grasp frame with newly introduced objects. This approach is agnostic to the global geometry of the object and is able to adapt when undesirable contact conditions, such as sliding, occur. The learning-based methodology estimates the non-linearities representative in the properties of the system. We test our framework physically on an adapted Yale Openhand Model O. By transferring the learned model from simulation to the physical hand without adaptation, we show that this energy modeling approach is robust to inaccuracies in parameter estimation. We demonstrate its efficacy in a handwriting task.

ICRA Conference 2019 Conference Paper

Energy Gradient-Based Graphs for Planning Within-Hand Caging Manipulation

Walter G. Bircher
Andrew S. Morgan
Kaiyu Hang
Aaron M. Dollar

In this work, we present a within-hand manipulation approach that leverages a simple energy model based on caging grasps made by underactuated hands. Instead of explicitly modeling the contacts and dynamics in manipulation, we can calculate a map to describe the energy states of different hand-object configurations under an actuation input. Since the system intrinsically steers towards low energy states, the object's movement is uniquely described by the gradient of the energy map if the corresponding actuation is applied. Such maps are pre-calculated for a range of actuation inputs to represent the system's energy profile. We discretize the workspace into a grid and construct an energy gradient-based graph by locally exploring the gradients of the stored energy profile. Given a goal configuration of a simple cylindrical object, a sequence of actuation inputs can be calculated to manipulate it towards the goal by exploiting the connectivity in the graph. The proposed approach is experimentally implemented on a Yale T42 hand. Our evaluation results show that parts of the graph are well connected, explaining our ability to successfully plan and execute trajectories within the gripper's workspace.

IROS Conference 2019 Conference Paper

Object Placement Planning and optimization for Robot Manipulators

Joshua A. Haustein
Kaiyu Hang
Johannes A. Stork
Danica Kragic

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

ICRA Conference 2019 Conference Paper

Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation

Weihao Yuan 0001
Kaiyu Hang
Haoran Song
Danica Kragic
Michael Yu Wang
Johannes A. Stork

Moving a human body or a large and bulky object may require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed- rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a robot behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

IROS Conference 2018 Conference Paper

Dexterous Manipulation Graphs

Silvia Cruciani
Christian Smith
Danica Kragic
Kaiyu Hang

We propose the Dexterous Manipulation Graph as a tool to address in-hand manipulation and reposition an object inside a robot's end-effector. This graph is used to plan a sequence of manipulation primitives so to bring the object to the desired end pose. This sequence of primitives is translated into motions of the robot to move the object held by the end-effector. We use a dual arm robot with parallel grippers to test our method on a real system and show successful planning and execution of in-hand manipulation.

ICRA Conference 2018 Conference Paper

Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

Weihao Yuan 0001
Johannes A. Stork
Danica Kragic
Michael Yu Wang
Kaiyu Hang

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

ICRA Conference 2017 Conference Paper

Integrating motion and hierarchical fingertip grasp planning

Joshua A. Haustein
Kaiyu Hang
Danica Kragic

In this work, we present an algorithm that simultaneously searches for a high quality fingertip grasp and a collision-free path for a robot hand-arm system to achieve it. The algorithm combines a bidirectional sampling-based motion planning approach with a hierarchical contact optimization process. Rather than tackling these problems in a decoupled manner, the grasp optimization is guided by the proximity to collision-free configurations explored by the motion planner. We implemented the algorithm for a 13-DoF manipulator and show that it is capable of efficiently planning reachable high quality grasps in cluttered environments. Further, we show that our algorithm outperforms a decoupled integration in terms of planning runtime.

ICRA Conference 2016 Conference Paper

On the evolution of fingertip grasping manifolds

Kaiyu Hang
Joshua A. Haustein
Miao Li 0002
Aude Billard
Christian Smith
Danica Kragic

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

ICRA Conference 2014 Conference Paper

Combinatorial optimization for hierarchical contact-level grasping

Kaiyu Hang
Johannes A. Stork
Florian T. Pokorny
Danica Kragic

We address the problem of generating force-closed point contact grasps on complex surfaces and model it as a combinatorial optimization problem. Using a multilevel refinement metaheuristic, we maximize the quality of a grasp subject to a reachability constraint by recursively forming a hierarchy of increasingly coarser optimization problems. A grasp is initialized at the top of the hierarchy and then locally refined until convergence at each level. Our approach efficiently addresses the high dimensional problem of synthesizing stable point contact grasps while resulting in stable grasps from arbitrary initial configurations. Compared to a sampling-based approach, our method yields grasps with higher grasp quality. Empirical results are presented for a set of different objects. We investigate the number of levels in the hierarchy, the computational complexity, and the performance relative to a random sampling baseline approach.

IROS Conference 2014 Conference Paper

Hierarchical Fingertip Space for multi-fingered precision grasping

Kaiyu Hang
Johannes A. Stork
Danica Kragic

Dexterous in-hand manipulation of objects benefits from the ability of a robot system to generate precision grasps. In this paper, we propose a concept of Fingertip Space and its use for precision grasp synthesis. Fingertip Space is a representation that takes into account both the local geometry of object surface as well as the fingertip geometry. As such, it is directly applicable to the object point cloud data and it establishes a basis for the grasp search space. We propose a model for a hierarchical encoding of the Fingertip Space that enables multilevel refinement for efficient grasp synthesis. The proposed method works at the grasp contact level while not neglecting object shape nor hand kinematics. Experimental evaluation is performed for the Barrett hand considering also noisy and incomplete point cloud data.

IROS Conference 2013 Conference Paper

Friction coefficients and grasp synthesis

Kaiyu Hang
Florian T. Pokorny
Danica Kragic

We propose a new concept called friction sensitivity which measures how susceptible a specific grasp is to changes in the underlying friction coefficients. We develop algorithms for the synthesis of stable grasps with low friction sensitivity and for the synthesis of stable grasps in the case of small friction coefficients. We describe how grasps with low friction sensitivity can be used when a robot has an uncertain belief about friction coefficients and study the statistics of grasp quality under changes in those coefficients. We also provide a parametric estimate for the distribution of grasp qualities and friction sensitivities for a uniformly sampled set of grasps.

IROS Conference 2012 Conference Paper

Improving generalization for 3D object categorization with Global Structure Histograms

Marianna Madry
Carl Henrik Ek
Renaud Detry
Kaiyu Hang
Danica Kragic

We propose a new object descriptor for three dimensional data named the Global Structure Histogram (GSH). The GSH encodes the structure of a local feature response on a coarse global scale, providing a beneficial trade-off between generalization and discrimination. Encoding the structural characteristics of an object allows us to retain low local variations while keeping the benefit of global representativeness. In an extensive experimental evaluation, we applied the framework to category-based object classification in realistic scenarios. We show results obtained by combining the GSH with several different local shape representations, and we demonstrate significant improvements to other state-of-the-art global descriptors.