We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-level motion generation conditioned on the specified goal. At the core of our approach is a two-level scene graph representation, namely geometric scene graph and symbolic scene graph. This hierarchical representation serves as a structured, object-centric abstraction of manipulation scenes. Our model uses graph neural networks to process these scene graphs for predicting high-level task plans and low-level motions. We demonstrate that our method scales to long-horizon tasks and generalizes well to novel task goals. We validate our method in a kitchen storage task in both physical simulation and the real world. Our experiments show that our method achieved over 70% success rate and nearly 90% of subgoal completion rate on the real robot while being four orders of magnitude faster in computation time compared to standard search-based task-and-motion planner.
Lang2Manip: A Tool for LLM-Based Symbolic-to-Geometric Planning for Manipulation
Khalifa University Center for Autonomous Robotic Systems (KUCARS)（卡利法大学自主机器人系统中心）；Khalifa University（卡利法大学）；Institute of Industrial and Control Engineering (IOC)（工业与控制工程研究所）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）
Clemson University（克莱姆森大学）；Air Force Research Laboratory（美国空军研究实验室）
Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this paper, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decom…
Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the hole, the robot should push the peg downwards. While high level execution frameworks such as state machines and behavior trees are commonly used to formalize such decision-making problems, these frameworks require a mechanism to detect the high-level symbolic state. Handcrafting heuristics to identify symbolic states can be brittle, and using data-driven methods can produce noisy predictions, particularly when worki…
Lehigh University（利哈伊大学）；Eindhoven University of Technology（埃因霍温理工大学）
This work presents a step towards utilizing incrementally-improving symbolic perception knowledge of the robot's surroundings for provably correct reactive control synthesis applied to an autonomous driving problem. Combining abstract models of motion control and information gathering, we show that assume-guarantee specifications (a subclass of Linear Temporal Logic) can be used to define and resolve traffic rules for cautious planning. We propose a novel representation called symbolic refinement tree for perception that captures the incremental knowledge about the environment and embodies the…
The Price Is Not Right: Neuro-Symbolic Methods Outperform VLAs on Structured Long-Horizon Manipulation Tasks with Significantly Lower Energy Consumption
Human-Robot Interaction Lab, Tufts University（Tufts大学人机交互实验室）；AIT Austrian Institute of Technology GmbH（奥地利技术研究所）
Laboratory for Intelligent Decision and Autonomous Robots, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院乔治·W·伍德拉夫机械工程学院智能决策与自主机器人实验室）；Georgia Tech Research Institute（佐治亚理工学院研究学院（Georgia Tech Research Institute））
This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamically changing environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Alternating Direction Method of Mult…
Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many variables but cannot represent the geometric and kinematic constraints required in manipulation. We present the FFRob algorithm for solving task and motion planning problems. First, we introduce Extended Acti…
Spatial AI & Robotics Lab, Department of Computer Science and Engineering, University at Buffalo（空间人工智能与机器人实验室，计算机科学与工程系，布法罗大学）；Carnegie Mellon University（卡内基梅隆大学）
TU Berlin（柏林工业大学）；Technion（以色列理工学院）；IBM Research（IBM研究院）
Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that connects logic planning with nonlinear optimization through a bidirectional interface, achieved by the detection of minimal subsets of nonlinear constraints that are infeasible. The algorithm continuously builds a database of graphs that r…
Robot manipulation in cluttered environments often 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 h…
Clemson University（克莱姆森大学）；U.S. Army CCDC Ground Vehicle Systems Center（美国陆军作战能力发展司令部地面车辆系统中心）
Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constr…
This work introduces PokeRRT, a novel motion planning algorithm that demonstrates poking as an effective non-prehensile manipulation skill to enable fast manipulation of objects and increase the size of a robot's reachable workspace. Our qualitative and quantitative results demonstrate the advantages of poking over pushing and grasping in planning object trajectories through uncluttered and cluttered environments.
Language-Augmented Symbolic Planner for Open-World Task Planning
The University of Hong Kong（香港大学）；Chongqing Institute of Microelectronics Industry Technology, University of Electronic Science and Technology of China（电子科技大学 Chongqing Institute of Microelectronics Industry Technology）；Centre for Transformative Garment Production (TransGP)（变革性服装生产中心（TransGP））；Southern University of Science and Technology（南方科技大学）；Texas A&M University（德克萨斯农工大学）
University of Freiburg（弗莱堡大学）；University of Bremen（不来梅大学）
Service robots in the future need to execute abstract instructions such as "fetch the milk from the fridge". To translate such instructions into actionable plans, robots require in-depth background knowledge. With regards to interactions with doors and drawers, robots require articulation models that they can use for state estimation and motion planning. Existing frameworks model articulated connections as abstract concepts such as prismatic, or revolute, but do not provide a parameterized model of these connections for computation. In this paper, we introduce a novel framework that uses symbo…
The path planning problems arising in manipulation planning and in task and motion planning settings are typically repetitive: the same manipulator moves in a space that only changes slightly. Despite this potential for reuse of information, few planners fully exploit the available information. To better enable this reuse, we decompose the collision checking into reusable, and non-reusable parts. We then treat the sequences of path planning problems in manipulation planning as a multiquery path planning problem. This allows the usage of planners that actively minimize planning effort over mult…
This paper presents Contact Mode Guided Manipulation Planning (CMGMP) for 3D quasistatic and quasidynamic rigid body motion planning in dexterous manipulation. The CMGMP algorithm generates hybrid motion plans including both continuous state transitions and discrete contact mode switches, without the need for pre-specified contact sequences or pre-designed motion primitives. The key idea is to use automatically enumerated contact modes of environment-object contacts to guide the tree expansions during the search. Contact modes automatically synthesize manipulation primitives, while the samplin…
CNRS（法国国家科学研究中心）；LAAS（系统分析与体系结构实验室）；Courant Institute, NYU（纽约大学库朗研究所）
Consider the problem of planning collision-free motion of $n$ objects in the plane movable through contact with a robot that can autonomously translate in the plane and that can move a maximum of $m \leq n$ objects simultaneously. This represents the abstract formulation of a manipulation planning problem that is proven to be decidable in this paper. The tools used for proving decidability of this simplified manipulation planning problem are, in fact, general enough to handle the decidability problem for the wider class of systems characterized by a stratified configuration space. These includ…
Tandon School of Engineering, New York University（纽约大学坦登工程学院）
This paper presents an efficient approach to object manipulation planning using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient ADMM-based trajectory optimization algorithm to evaluate the dynamic feasibility of candidate contact sequences. To accelerate MCTS, we propose a methodology to learn a goal-conditioned policy-value network to direct the search towards promising nodes. Further, manipulation-specific heuristics enable to drastically reduce the search space. Systematic object manipulation experiments in a physics simulator and on real hardware demonstrate the e…
University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）
This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner tha…
Institute of Industrial and Control Engineering (IOC)（工业与控制工程研究所）；Universitat Politcnica de Catalunya (UPC) – Barcelona Tech（加泰罗尼亚理工大学 – 巴塞罗那科技大学）
Robotic manipulation involves actions where contacts occur between the robot and the objects. In this scope, the availability of physics-based engines allows motion planners to comprise dynamics between rigid bodies, which is necessary for planning this type of actions. However, physics-based motion planning is computationally intensive due to the high dimensionality of the state space and the need to work with a low integration step to find accurate solutions. On the other hand, manipulation actions change the environment and conditions further actions and motions. To cope with this issue, th…
The discontinuities and multi-modality introduced by contacts make manipulation planning challenging. Many previous works avoid this problem by pre-designing a set of high-level motion primitives like grasping and pushing. However, such motion primitives are often not adequate to describe dexterous manipulation motions. In this work, we propose a method for dexterous manipulation planning at a more primitive level. The key idea is to use contact modes to guide the search in a sampling-based planning framework. Our method can automatically generate contact transitions and motion trajectories un…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons. In this work, we address these challenges with Neuro-Symbolic Relational Transition Models (NSRTs), a novel class of models that are data-efficient to learn, compatible with powerful robotic planning methods, and generalizable over objects. NSRTs have both symbolic and neural components, enabling a bilevel planning scheme where symbolic AI planning in an outer loop guides continuous planning with neural models in an inner loop. Experiments in four robotic plan…
Extracting Visual Plans from Unlabeled Videos via Symbolic Guidance
Department of Electrical Engineering and Automation, Aalto University（艾尔沃斯大学电气工程与自动化系）；Department of Engineering and Computer Science, University of Trento（特伦托大学工程与计算机科学系）
Humanoid Loco-manipulation Planning based on Graph Search and Reachability Maps
CNRS-AIST JRL (Joint Robotics Laboratory)（CNRS-AIST联合机器人实验室）；IRL（机器人研究所）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
Manipulation in clutter requires solving complex sequential decision making problems in an environment rich with physical interactions. The transfer of motion planning solutions from simulation to the real world, in open-loop, suffers from the inherent uncertainty in modelling real world physics. We propose interleaving planning and execution in real-time, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for efficient planning, and for estimating the…
Osaka University（大阪大学）；National Inst. of AIST（国立产业技术综合研究所）
This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single and dual arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph which encodes grasp configurations and object poses. The algorith…
Neuro-Symbolic Manipulation Understanding with Enriched Semantic Event Chains
Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran（德黑兰大学数学、统计学与计算机科学学院计算机科学系）；Computer Science, College of Science, University of Tehran（德黑兰大学科学学院计算机科学系）
We present a damage-aware planning approach which determines the best sequence to manipulate a number of objects in a scene. This works on task-planning level, abstracts from motion planning and anticipates the dynamics of the scene using a physics simulation. Instead of avoiding interaction with the environment, we take unintended motion of other objects into account and plan manipulation sequences which minimize the potential damage. Our method can also be used as a validation measure to judge planned motions for their feasibility in terms of damage avoidance. We evaluate our approach on one…
Dipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli（坎帕尼亚路易吉·凡维泰利大学工程系）；Institute for Artificial Intelligence, Universität Bremen（不来梅大学人工智能研究所）
Manipulation planning and control are relevant building blocks of a robotic system and their tight integration is a key factor to improve robot autonomy and allows robots to perform manipulation tasks of increasing complexity, such as those needed in the in-store logistics domain. Supermarkets contain a large variety of objects to be placed on the shelf layers with specific constraints, doing this with a robot is a challenge and requires a high dexterity. However, an integration of reactive grasping control and motion planning can allow robots to perform such tasks even with grippers with limi…
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipulator arm to avoid obstacles in the environment and plan an end-effector pose for grasping. While trajectory planning and grasp planning are often tackled separately, how to efficiently integrate the two planning problems remains a challenge. In this work, we present a novel method for joint motion and grasp planning. Our method integrates manipulation trajectory optimization with online grasp synthesis and selection, …
Manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasping and placing an object, or more general tool-use. To achieve such interactions, traditional approaches require hand-engineering of object representations and interaction constraints, which easily becomes tedious when complex objects/interactions are considered. Inspired by recent advances in 3D modeling, e.g. NeRF, we propose a method to represent objects as continuous functions upon which constraint features are defined and jointly trained. In …
