35 accepted papers
Multi-robot motion planning for high degree-offreedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled methods plan directly in the composite configuration space, which scales poo
Dexterous manipulation is physics-intensive and highly sensitive to modeling errors and perception noise, making sim-to-real transfer prohibitively challenging. Domain randomization (DR) is commonly used to improve the robustness of learned policies for such tasks, but conventional DR randomizes one…
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
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,
Robotic manipulation research has investigated contact-rich problems and strategies that require robots to intentionally collide with their environment, to accomplish tasks that cannot be handled by traditional collision-free solutions. By enabling compliant robot motions, collisions between the rob
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 cont
Robust and adaptive robotic peg-in-hole assembly under tight tolerance is critical to various industrial applications. Still, it remains an open challenge due to perception and physical uncertainties from contact-rich interactions that easily exceed the allowed clearance. In this paper, we study how…
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 rem
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,
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 manipu…
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 requirement…
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-scal…
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 sys…
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 incorpo…
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.…
We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches either grasp or push an object and then obtain the segmentation mask of the grasped or pushed object after one action. I…
Constraining contacts to remain fixed on an object during manipulation limits the potential workspace size, as motion is subject to the hand’s kinematic topology. Finger gaiting is one way to alleviate such restraints. It allows contacts to be freely broken and remade so as to operate on different m
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…
In this work, we propose a pick-and-place benchmark to assess the manipulation capabilities of a robotic system. The benchmark is based on the Box and Blocks Test (BBT), a task utilized for decades by the rehabilitation community to assess unilateral gross manual dexterity in humans. We propose thre
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh f
Numerous grasp planning algorithms have been proposed since the 1980s. The grasping literature has expanded rapidly in recent years, building on greatly improved vision systems and computing power. Methods have been proposed to plan stable grasps on known objects (exact 3D model is available), famil
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…
We address the problem of controlling a partially constrained trajectory of the manipulation frame-an arbitrary frame of reference rigidly attached to the object-as the desired motion about this frame is often underdefined. This may be apparent, for example, when the task requires control only about
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 sy…
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-objec…
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…
We address the problem of pregrasp sliding manipulation, which is an essential skill when a thin object cannot be directly grasped from a flat surface. Leveraged on the passive reconfigurability of soft, compliant, or underactuated robotic hands, we formulate this problem as an integrated motion and
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…
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 int…
We address the problem of fingertip design by leveraging on the fact that most grasp contacts share a few classes of local geometries. In order to maximize the contact areas for achieving more robust grasps, we first define the concept of Contact Primitive, which represents a set of contacts of simi
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, a…
We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all gras
In this paper, we propose a solution to the problem of {\it herding by caging}: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we move the latter to some predefined location in such a way that they cannot escape from the robots while moving. We model the…
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.…
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 exec