A deep understanding of kinematic structures is essential for robot motion and interaction with the environment. Such understanding is captured through articulated objects, which are essential for physical simulation, motion planning, and policy learning. However, creating these models, particularly for objects with high degrees of freedom (DoF), remains a significant challenge. Existing methods typically rely on motion sequences or strong assumptions from hand-curated datasets. In this paper, we introduce Kinematify, an automated framework that synthesizes articulated objects from arbitrary RGB images or textual descriptions. Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static 3D geometry. To achieve this, we combine MCTS search for structural inference with geometry-driven optimization for joint reasoning, producing physically consistent and functionally valid models. We evaluate Kinematify on diverse inputs from both synthetic environments and real-world, demonstrating improvements in registration and kinematic topology accuracy over prior work.

Motion planning is a fundamental problem in robotics that involves generating feasible trajectories for a robot to follow. Recent advances in parallel computing, particularly through CPU and GPU architectures, have significantly reduced planning times to the order of milliseconds. However, constrained motion planning especially using sampling based methods on GPUs remains underexplored. Prior work such as pRRTC leverages a tracking compiler with a CUDA backend to accelerate forward kinematics and collision checking. While effective in simple settings, their approach struggles with increased complexity in robot models or environments. In this paper, we propose a novel GPU based framework utilizing NVRTC for runtime compilation, enabling efficient handling of high complexity scenarios and supporting constrained motion planning. Experimental results demonstrate that our method achieves superior performance compared to existing approaches.

A deep understanding of kinematic structures and movable components is essential for enabling robots to manipulate objects and model their own articulated forms. Such understanding is captured through articulated objects, which are essential for tasks such as physical simulation, motion planning, and policy learning. However, creating these models, particularly for complex systems like robots or objects with high degrees of freedom (DoF), remains a significant challenge. Existing methods typically rely on motion sequences or strong assumptions from hand-curated datasets, which hinders scalability. In this paper, we introduce Kinematify, an automated framework that synthesizes articulated objects directly from arbitrary RGB images or text prompts. Our method addresses two core challenges: (i) inferring kinematic topologies for high-DoF objects and (ii) estimating joint parameters from static geometry. To achieve this, we combine MCTS search for structural inference with geometry-driven optimization for joint reasoning, producing physically consistent and functionally valid descriptions. We evaluate Kinematify on diverse inputs from both synthetic and real-world environments, demonstrating improvements in registration and kinematic topology accuracy over prior work.

Efficient tabletop rearrangement planning seeks to find high-quality solutions while minimizing total cost. However, the task is challenging due to object dependencies and limited buffer space for temporary placements. The complexity increases for mobile robots, which must navigate around the table with restricted access. A*-based methods yield high-quality solutions, but struggle to scale as the number of objects increases. Monte Carlo Tree Search (MCTS) has been introduced as an anytime algorithm, but its convergence speed to high-quality solutions remains slow. Previous work~\cite{strap2024} accelerated convergence but required the robot to move to the closest position to the object for each pick and place operation, leading to inefficiencies. To address these limitations, we extend the planner by introducing a more efficient strategy for mobile robots. Instead of selecting the nearest available location for each action, our approach allows multiple operations (e.g., pick-and-place) from a single standing position, reducing unnecessary movement. Additionally, we incorporate state re-exploration to further improve plan quality. Experimental results show that our planner outperforms existing planners both in terms of solution quality and planning time.

We consider the problem of building an assistive robotic system that can help humans in daily household cleanup tasks. Creating such an autonomous system in real-world environments is inherently quite challenging, as a general solution may not suit the preferences of a particular customer. Moreover, such a system consists of multi-objective tasks comprising - (i) Detection of misplaced objects and prediction of their potentially correct placements, (ii) Fine-grained manipulation for stable object grasping, and (iii) Room-to-room navigation for transferring objects in unseen environments. This work systematically tackles each component and integrates them into a complete object rearrangement pipeline. To validate our proposed system, we conduct multiple experiments on a real robotic platform involving multi-room object transfer, user preference-based placement, and complex pick-and-place tasks. Additional details including video demonstrations of our work are available at https://sites.google.com/eng.ucsd.edu/home-robot.

Place-and-pick-based re-grasping is an in-hand object manipulation strategy designed for a simple gripper. In this paper, we address the problem of place-and-pick-based re-grasping on objects without stable intermediate placements. We propose a modified place-and-pick-based strategy by using unstable placements as intermediate placements for re-grasping. The robot can place the object in an unstable pose where the gripper can re-grasp the object while using the fingertips to constrain the object from moving. We demonstrate the experimental validity of the proposed re-grasping strategy in both simulation and the real world.

Nowadays, a number of grasping algorithms [1], [2] have been proposed, that can predict a candidate of grasp poses, even for unseen objects. This enables a robotic manipulator to pick-and-place such objects. However, some of the predicted grasp poses to stably lift a target object may not be directly approachable due to workspace limitations. In such cases, the robot will need to re-grasp the desired object to enable successful grasping on it. This involves planning a sequence of continuous actions such as sliding, re-grasping, and transferring. To address this multi-modal problem, we propose a Markov-Decision Process-based multi-modal planner that can rearrange the object into a position suitable for stable manipulation. We demonstrate improved performance in both simulation and the real world for pick-and-place tasks.

Due to their complexity, foliated structure problems often pose intricate challenges to task and motion planning in robotics manipulation. To counter this, our study presents the ``Foliated Repetition Roadmap.'' This roadmap assists task and motion planners by transforming the complex foliated structure problem into a more accessible graph format. By leveraging query experiences from different foliated manifolds, our framework can dynamically and efficiently update this graph. The refined graph can generate distribution sets, optimizing motion planning performance in foliated structure problems. In our paper, we lay down the theoretical groundwork and illustrate its practical applications through real-world examples.

We present a methodology for a service robot to put away the groceries in a precise and stable configuration that satisfies the desired semantic relationship of grocery and pantry objects. This task is invaluable to the motor impaired and increases the capability of contextualized service robots. We show that augmenting traditional geometric assessment with contextual knowledge of food categorization and a language association model, allows the robot to complete this task efficiently. We quantitatively validate our approach with a data set of 51 common grocery items, of which 11 objects are used for real world experiments. According to our evaluation, our method is able to successfully shelve objects next to semantically related objects 100% of the time when these relationships exist. We achieve this with an average placement precision of 3.2cm and a standard deviation of 1.1cm. We discuss remaining challenges and needed improvements for robots with these capabilities to be introduced to home environments.

Rotational slippage due to the gravitational torque is common in grasping tasks using a parallel gripper. To pick the object with a specific desirable grasp pose, if the parallel gripper cannot provide enough force to eliminate the gravitational torque, rotational slippage will cause the manipulation failure. This paper presents an approach to eliminate rotational slippage by pivoting an object on a supporting surface to a pose where it can be picked up, while minimizing the gravitational torque. For evaluation we compare the proposed method with a simple grasping approach, which lifts the object directly, without avoiding slippage. Extensive experiments conducted across both simulation, and real-world environments show that our approach can significantly reduce the rotational slippage problem and help in completing several pick-and-place tasks.

The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new tasks and to ensure robust operation in a semi-structured environment. We present a layered strategy to transform missions into tasks and actions and provide a set of strategies to address simple and complex failures. We propose a model for characterizing failures using this model and discuss repairs. Simple failures are by far the most common in our WRC system and we also present how we repaired them.

The manufacturing world is moving towards a setup with high mix and medium volume. This requires new production paradigms. The World Robotics Challenge (2018 & 2020) was designed to challenge teams to design systems that are easy to adapt to new tasks and to ensure robust operation in a semi-structured environment. We present a layered strategy to transform missions into tasks and actions and provide a set of strategies to address simple and complex failures. We propose a model for characterizing failures using this model and discuss repairs. Simple failures are by far the most common in our WRC system and we also present how we repaired them.

The World Robot Summit (WRS) 2020 Assembly Challenge is designed to allow teams to demonstrate how one can build flexible, robust systems for assembly of machined objects. We present our approach to assembly based on integration of machine vision, robust planning and execution using behavior trees and a hierarchy of recovery strategies to ensure robust operation. Our system was selected for the WRS 2020 Assembly Challenge finals based on robust performance in the qualifying rounds. We present the systems approach adopted for the challenge.

In the robotic industry, specular and textureless metallic components are ubiquitous. The 6D pose estimation of such objects with only a monocular RGB camera is difficult because of the absence of rich texture features. Furthermore, the appearance of specularity heavily depends on the camera viewpoint and environmental light conditions making traditional methods, like template matching, fail. In the last 30 years, pose estimation of the specular object has been a consistent challenge, and most related works require massive knowledge modeling effort for light setups, environment, or the object surface. On the other hand, recent works exhibit the feasibility of 6D pose estimation on a monocular camera with convolutional neural networks(CNNs) however they mostly use opaque objects for evaluation. This paper provides a data-driven solution to estimate the 6D pose of specular objects for grasping them, proposes a cost function for handling symmetry, and demonstrates experimental results showing the system's feasibility.