53 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
It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain perception constraints, such as monitoring people or multiple objects, for safety and reliability while executing its ta
The central line dressing change is a life-critical procedure performed by nurses to provide patients with rapid infusion of fluids, such as blood and medications. Due to their complexity and the heavy workloads nurses face, dressing changes are prone to preventable errors that can result in centra…
Large Language Models (LLMs) have demonstrated remarkable ability in long-horizon Task and Motion Planning (TAMP) by translating clear and straightforward natural language problems into formal specifications such as the Planning Domain Definition Language (PDDL). However, real-world problems are oft
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations ar
Long-horizon task planning is important for robot autonomy, especially as a subroutine for frameworks such as Integrated Task and Motion Planning. However, task planning is computationally challenging and struggles to scale to realistic problem settings. We propose to accelerate task planning over a…
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision checking. We present a novel spatial data structure…
Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom problems. This paper presents performance improvements of more than 500x over the state-of-the-art, bringing planning times…
Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes signifi-cantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We propose a control framework that solves the motion-planning problem for multi-robot…
As robots become more prevalent, the complexity of robot-robot, robot-human, and robot-environment interactions increases. In these interactions, a robot needs to consider not only the effects of its own actions, but also the effects of other agents’ actions and the possible interactions between age…
Motion planning under sensing uncertainty is critical for robots in unstructured environments, to guarantee safety for both the robot and any nearby humans. Most work on planning under uncertainty does not scale to high-dimensional robots such as manipulators, assumes simplified geometry of the robo…
Robotic deployments in human environments have motivated the need for autonomous systems to be able to interact with humans and solve tasks effectively. Human demonstrations of tasks can be used to infer underlying task specifications, commonly modeled with temporal logic. State-of-the-art methods h…
Robotic task planning is computationally challenging. To reduce planning cost and support life-long operation, we must leverage prior planning experience. To this end, we address the problem of extracting reusable and generalizable abstract skills from successful plan executions. In previous work, w…
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…
3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired final configuration. Object reconfiguration is challenging to solve in general, as it requires efficient reasoning about…
Many methods that solve robot planning problems, such as task and motion planners, employ discrete symbolic search to find sequences of valid symbolic actions that are grounded with motion planning. Much of the efficacy of these planners lies in this grounding-bad placement and grasp choices can lea…
Robotics and automation are poised to change the landscape of home and work in the near future. Robots are adept at deliberately moving, sensing, and interacting with their environments. The pervasive use of robotics promises societal and economic payoffs due to its capabilities—conversely, the capa…
Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically <italic xmlns:mml="http://www.w3.org/1998
Sampling-based motion planners are widely used for motion planning with high- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dof</small> robots. These planners generally rely on a uniform distribution to explore the search space. Recent work has explore
Learning state representations enables robotic planning directly from raw observations such as images. Several methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the…
Future robotic deployments will require robots to be able to repeatedly solve a variety of tasks in application domains. Task and motion planning addresses complex robotic problems that combine discrete reasoning over states and actions and geometric interactions during action executions. Moving bey…
Motion planning is a core problem in robotics, with a range of existing methods aimed to address its diverse set of challenges. However, most existing methods rely on complete knowledge of the robot environment; an assumption that seldom holds true due to inherent limitations of robot perception. To…
Recent work has demonstrated that motion planners' performance can be significantly improved by retrieving past experiences from a database. Typically, the experience database is queried for past similar problems using a similarity function defined over the motion planning problems. However, to date…
Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for
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…
Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular Moveit library and Robot Operating System (ROS) middleware. Robowflex provides an augmented API for crafting and manipulating motion planning queries within a single program, mak…
Many manipulation tasks combine high-level discrete planning over actions with low-level motion planning over continuous robot motions. Task and motion planning (TMP) provides a powerful general framework to combine discrete and geometric reasoning, and solvers have been previously proposed for sing…
We present a framework for planning complex motor actions such as pouring or scooping from arbitrary start states in cluttered real-world scenes. Traditional approaches to such tasks use dynamic motion primitives (DMPs) learned from human demonstrations. We enhance a recently proposed state-of-the-a…
Using sampling to estimate the connectivity of high-dimensional configuration spaces has been the theoretical underpinning for effective sampling-based motion planners. Typical strategies either build a roadmap, or a tree as the underlying search structure that connects sampled configurations, with…
Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of hu…
Over the years, many motion planning algorithms have been proposed. It is often unclear which algorithm might be best suited for a particular class of problems. The problem is compounded by the fact that algorithm performance can be highly dependent on parameter settings. This paper shows that hyper…
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large…
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferable to leverage previously computed motion plans, i.e., experiences, to ease the planning. Different approaches have been
Motion planning for high degree-of-freedom (DOF) robots is challenging, especially when acting in complex environments under sensing uncertainty. While there is significant work on how to plan under state uncertainty for low-DOF robots, existing methods cannot be easily translated into the high-DOF…
Many robotic manipulation problems are multi-modal—they consist of a discrete set of mode families (e.g., whether an object is grasped or placed) each with a continuum of parameters (e.g., where exactly an object is grasped). Core to these problems is solving single-mode motion plans, i.e., given a…
This work proposes a novel method of incorporating calls to a motion planner inside a potential field control policy for safe multi-robot navigation with uncertain dynamics. The proposed framework can handle more general scenes than the control policy and has low computational costs. Our work is rob…
Recently, there has been a growing interest in robotic systems that are able to share workspaces and collaborate with humans. Such collaborative scenarios require efficient mechanisms to communicate human requests to a robot, as well as to transmit robot interpretations and intents to humans. Recent
Robotic manipulation problems are inherently continuous, but typically have underlying discrete structure, e.g., whether or not an object is grasped. This means many problems are multi-modal and in particular have a continuous infinity of modes. For example, in a pick-and-place manipulation domain,…
When robotic manipulators perform high-level tasks in the presence of another agent, e.g., a human, they must have a strategy that considers possible interferences in order to guarantee task completion and efficient resource usage. One approach to generate such strategies is called reactive synthesi
When humans and robots perform complex tasks together, the robot must have a strategy to choose its actions based on observed human behavior. One well-studied approach for finding such strategies is reactive synthesis. Existing approaches for finite-horizon tasks have used an explicit state approach…
Nowadays robotic systems are expected to share workspaces and collaborate with humans. In such collaborative environments, an important challenge is to ground or establish the correct semantic interpretation of a human request. Once such an interpretation is available, the request must be translated…
Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners pr
Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot’s onboard computer is challenging due to the limited com…
Effectively planning robust executions under uncertainty is critical for building autonomous robots. Partially observable Markov decision processes (POMDPs) provide a standard framework for modeling many robot applications under uncertainty. We study POMDPs with two kinds of objectives: (1) Boolean
Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information,…
We present the first platform-independent evaluation method for task and motion planning (TAMP). Previously point, various problems have been used to test individual planners for specific aspects of TAMP. However, no common set of metrics, formats, and problems have been accepted by the community. W
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This letter takes a different approach and proposes to a
There are many applications where robots have to operate in environments that other agents can change. In such cases, it is desirable for the robot to achieve a given high-level task despite interference. Ideally, the robot must decide its next action as it observes the changes in the world, i.e. ac…
Recent progress in motion planning has made it possible to determine homotopy inequivalent trajectories between an initial and terminal configuration in a robot configuration space. Current approaches have however either assumed the knowledge of differential one-forms related to a skeletonization of…
We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstrac- tions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved per- formance and…
We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now…
We present an algorithm that seeks to find a set of diverse, short paths through a roadmap graph. The usefulness of a such a set is illustrated in robotic motion planning and routing applications wherein a precomputed roadmap of the environment is partially invalidated by some change, for example, r…
Manipulation planning from high-level task specifications, even though highly desirable, is a challenging problem. The large dimensionality of manipulators and complexity of task specifications make the problem computationally intractable. This work introduces a manipulation planning framework with…