2

We propose a collision-inclusive planning framework for occluded object grasping that allows and exploits intentional collisions through compliant robot motions, demonstrating effective manipulation in scenarios where traditional collision-free approaches are insufficient.

We propose a novel concept called “Caging in Time” that enables robust object manipulation with a single end-effector under uncertainties and limited perception. This approach allows caging configurations to be formed in time, demonstrating effective open-loop manipulation without requiring detailed object knowledge or real-time feedback.

The 2024 RGMC at ICRA featured five tracks testing autonomous robotic manipulation capabilities, drawing participation from teams worldwide to address challenges in manufacturing and essential manipulation skills.

We propose a novel object-centric planning paradigm for nonprehensile robot rearrangement manipulation. Our approach focuses on planning desired object motions, which are then realized via robot actions generated online. Experiments show that this paradigm generates more intuitive and efficient robot actions compared to traditional robot-centric approaches.

This work presents a method for achieving complete SO(3) finger gating control of grasped objects against gravity, using a manipulation planner that operates via orthogonal safe modes of a compliant, underactuated hand absent of tactile sensors or joint encoders. The method takes advantage of system compliance to allow the hand to more easily switch contacts while maintaining a stable grasp, and uses a low-latency 6D pose object tracker for online feedback and adaptive recovery from trajectory deviations. The method is demonstrated on a real robot manipulating both convex and non-convex objects, and represents a valuable step towards realizing true robot in-hand manipulation capabilities.

We propose a solution to the problem of herding by caging: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we guide the sheep to a target location without letting them escape from the herders along the way. We model the interaction between the herders and the sheep by defining virtual “repulsive forces” pushing the sheep away from the herders. This enables the herders to partially control the motion of the sheep. We formalize this behavior topologically by applying the notion of caging, a concept used in robotic manipulation. We demonstrate that our approach is provably correct in the sense that the sheep cannot escape from the robots under our assumed motion model. We propose an RRT-based path planning algorithm for herding by caging, demonstrate its probabilistic completeness, and evaluate it in simulations as well as on a group of real mobile robots.

The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand’s actuation input to the object’s motion to be ob-tained. Without assuming that most of these model parameters are known a priori or can be easily estimated by sensors, we focus on equipping robots with the ability to actively self-identify necessary model parameters using minimal sensing. Here, we derive algorithms, on the basis of the concept of virtual linkage-based representations (VLRs), to self-identify the underlying mechanics of hand-object systems via exploratory manipulation actions and probabilistic reasoning and, in turn, show that the self-identified VLR can enable the control of precise in-hand ma-nipulation. To validate our framework, we instantiated the proposed system on a Yale Model O hand without joint encoders or tactile sensors. The passive adaptability of the underactuated hand greatly facilitates the self-identification process, because they naturally secure stable hand-object interactions during random exploration. Relying solely on an in-hand camera, our system can effectively self-identify the VLRs, even when some fingers are replaced with novel designs. In addition, we show in-hand manipulation applications of handwriting, marble maze playing, and cup stacking to demonstrate the effectiveness of the VLR in precise in-hand manipulation control.

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 position of a handheld object using the fingers, environment, or a combination of both. We provide examples of initial and goal states (i.e., static object poses and fingertip locations) for various in-hand manipulation tasks, given an object surface mesh from the YCB dataset. We further propose metrics that measure the error in reaching the goal state from a specific initial state. When aggregated across all tasks, these metrics also serve as a measure of the system’s in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark.

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 that has been utilized for decades by the rehabilitation community to assess unilateral gross manual dexterity in humans. We propose three robot benchmarking protocols in this work that hold true to the spirit of the original clinical tests: the Modified-BBT, the Targeted-BBT, and the Standard-BBT. These protocols can be implemented by the greater robotics research community, as the physical BBT setup has been widely distributed with the Yale-CMU-Berkeley (YCB) Object and Model Set. The difficulty of the three protocols increases sequentially, adding a new performance component at each level, and therefore aiming to assess various aspects of the system separately. Clinical task-time norms are summarized for able-bodied human participants. We provide baselines for all three protocols with off-the-shelf planning and perception algorithms on a Barrett WAM and a Franka Emika Panda manipulator and compare the results with human performance.

“The support function of a surface” is a fundamental concept in mathematics and a crucial operation for algorithms in robotics, such as those for collision detection and grasp planning. It is possible to calculate the support function of a convex body in closed form. However, for complex continuous surfaces, especially non-convex ones, this calculation can be far more difficult, and no general solution is available so far. This limits the applicability of related algorithms. This paper presents a branch-and-bound (B&B) algorithm to calculate the support function of complex continuous surfaces. An upper bound of the support function over a surface domain is derived. While a surface domain is divided into subdomains, the upper bound of the support function over any subdomain is proved to be not greater than the one over the original domain. Then, as the B&B algorithm sequentially divides the surface domain by dividing its subdomain having a greater upper bound than the others, the maximum upper bound over all subdomains is monotonically decreasing and converges to the exact value of the desired support function. Furthermore, with the aid of the B&B algorithm, this paper derives new algorithms for the minimum distance between complex continuous surfaces and for globally optimal grasps on objects with continuous surfaces. A number of numerical examples are provided to demonstrate the effectiveness of the proposed algorithms.