ReplicaCADisonehousewith 111 layouts (6 fromrealscans, 1 05 artistcreated), The TDWTransporter Challenge [7] has 15 houses, ManipulaTHOR [33] has 30 rooms, RoomRfrom Visual Room Rearrangement [58] has 120 rooms, iGibson [36] has 15 houses, and VirtualHome [51] has 6 houses.

Abstract--Industrial automation with six-axis robotic arms is critical for many manufacturing tasks, including welding and additive manufacturing applications; however, many of these operations are functionally redundant due to the symmetrical tool axis, which effectively makes the operation a five-axis task. Exploiting this redundancy is crucial for achieving the desired workspace and dexterity required for the feasibility and optimisation of toolpath planning. Inverse kinematics algorithms can solve this in a fast, reactive framework, but these techniques are underutilised over the more computationally expensive offline planning methods. We propose a novel algorithm to solve functionally redundant inverse kinematics for robotic manipulation utilising a task space decomposition approach, the damped least-squares method and Halley's method to achieve fast and robust solutions with reduced joint motion. We evaluate our methodology in the case of toolpath optimisation in a cold spray coating application on a non-planar surface. The functionally redundant inverse kinematics algorithm can quickly solve motion plans that minimise joint motion, expanding the feasible operating space of the complex toolpath.

Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However, these methods mostly employ single-shot plans that are typically limited to simple motions, or iterative refinement which requires multiple calls to the video generator, incuring high computational cost. To overcome these limitations, we propose SketchVerify, a training-free, sketch-verification-based planning framework that improves motion planning quality with more dynamically coherent trajectories (i.e., physically plausible and instruction-consistent motions) prior to full video generation by introducing a test-time sampling and verification loop. Given a prompt and a reference image, our method predicts multiple candidate motion plans and ranks them using a vision-language verifier that jointly evaluates semantic alignment with the instruction and physical plausibility. To efficiently score candidate motion plans, we render each trajectory as a lightweight video sketch by compositing objects over a static background, which bypasses the need for expensive, repeated diffusion-based synthesis while achieving comparable performance. We iteratively refine the motion plan until a satisfactory one is identified, which is then passed to the trajectory-conditioned generator for final synthesis. Experiments on WorldModelBench and PhyWorldBench demonstrate that our method significantly improves motion quality, physical realism, and long-term consistency compared to competitive baselines while being substantially more efficient. Our ablation study further shows that scaling up the number of trajectory candidates consistently enhances overall performance.

Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation (`task') space of robotic systems. However, the realization of the generated motion plans is often compromised by a ''task-execution mismatch'', where unmodeled dynamics, persistent disturbances, and system latency cause the robot's actual task-space state to diverge from the desired motion trajectory. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.

Abstract-- In this work, we introduce MO-SeGMan, a Multi-Objective Sequential and Guided Manipulation planner for highly constrained rearrangement problems. MO-SeGMan generates object placement sequences that minimize both re-planning per object and robot travel distance while preserving critical dependency structures with a lazy evaluation method. T o address highly cluttered, non-monotone scenarios, we propose a Selective Guided Forward Search (SGFS) that efficiently relocates only critical obstacles and to feasible relocation points. Furthermore, we adopt a refinement method for adaptive subgoal selection to eliminate unnecessary pick-and-place actions, thereby improving overall solution quality. Extensive evaluations on nine benchmark rearrangement tasks demonstrate that MO-SeGMan generates feasible motion plans in all cases, consistently achieving faster solution times and superior solution quality compared to the baselines. These results highlight the robustness and scalability of the proposed framework for complex rearrangement planning problems. Supplementary videos and code are available at: https: //sites.google.com/view/mo-segman/. Rearrangement planning involves generating a feasible motion plan for a robot to move all goal objects to their designated locations.

Generating large and diverse obstacle datasets to learn motion planning in environments with dynamic obstacles is challenging due to the vast space of possible obstacle trajectories. Inspired by hallucination-based data synthesis approaches, we propose Learning from Hallucinating Critical Points (LfH-CP), a self-supervised framework for creating rich dynamic obstacle datasets based on existing optimal motion plans without requiring expensive expert demonstrations or trial-and-error exploration. LfH-CP factorizes hallucination into two stages: first identifying when and where obstacles must appear in order to result in an optimal motion plan, i.e., the critical points, and then procedurally generating diverse trajectories that pass through these points while avoiding collisions. This factorization avoids generative failures such as mode collapse and ensures coverage of diverse dynamic behaviors. We further introduce a diversity metric to quantify dataset richness and show that LfH-CP produces substantially more varied training data than existing baselines. Experiments in simulation demonstrate that planners trained on LfH-CP datasets achieves higher success rates compared to a prior hallucination method.

When a human dyad jointly manipulates an object, they must communicate about their intended motion plans. Some of that collaboration is achieved through the motion of the manipulated object itself, which we call "haptic communication." In this work, we captured the motion of human-human dyads moving an object together with one participant leading a motion plan about which the follower is uninformed. We then captured the same human participants manipulating the same object with a robot collaborator. By tracking the motion of the shared object using a low-cost IMU, we can directly compare human-human shared manipulation to the motion of those same participants interacting with the robot. Intra-study and post-study questionnaires provided participant feedback on the collaborations, indicating that the human-human collaborations are significantly more fluent, and analysis of the IMU data indicates that it captures objective differences in the motion profiles of the conditions. The differences in objective and subjective measures of accuracy and fluency between the human-human and human-robot trials motivate future research into improving robot assistants for physical tasks by enabling them to send and receive anthropomorphic haptic signals.

We present an inverse dynamic game-based algorithm to learn parametric constraints from a given dataset of local generalized Nash equilibrium interactions between multiple agents. Specifically, we introduce mixed-integer linear programs (MILP) encoding the Karush-Kuhn-Tucker (KKT) conditions of the interacting agents, which recover constraints consistent with the Nash stationarity of the interaction demonstrations. We establish theoretical guarantees that our method learns inner approximations of the true safe and unsafe sets, as well as limitations of constraint learnability from demonstrations of Nash equilibrium interactions. We also use the interaction constraints recovered by our method to design motion plans that robustly satisfy the underlying constraints. Across simulations and hardware experiments, our methods proved capable of inferring constraints and designing interactive motion plans for various classes of constraints, both convex and non-convex, from interaction demonstrations of agents with nonlinear dynamics.

The advent of end-to-end autonomy stacks - often lacking interpretable intermediate modules - has placed an increased burden on ensuring that the final output, i.e., the motion plan, is safe in order to validate the safety of the entire stack. This requires a safety monitor that is both complete (able to detect all unsafe plans) and sound (does not flag safe plans). In this work, we propose a principled safety monitor that leverages modern multi-modal trajectory predictors to approximate forward reachable sets (FRS) of surrounding agents. By formulating a convex program, we efficiently extract these data-driven FRSs directly from the predicted state distributions, conditioned on scene context such as lane topology and agent history. To ensure completeness, we leverage conformal prediction to calibrate the FRS and guarantee coverage of ground-truth trajectories with high probability. To preserve soundness in out-of-distribution (OOD) scenarios or under predictor failure, we introduce a Bayesian filter that dynamically adjusts the FRS conservativeness based on the predictor's observed performance. We then assess the safety of the ego vehicle's motion plan by checking for intersections with these calibrated FRSs, ensuring the plan remains collision-free under plausible future behaviors of others. Extensive experiments on the nuScenes dataset show our approach significantly improves soundness while maintaining completeness, offering a practical and reliable safety monitor for learned autonomy stacks.

-- Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. T o address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi-Agent Motion Planning. Our method provides scalable neural HJR modeling to tackle high-dimensional configuration spaces and capture worst-case collision and safety constraints between agents. We further propose a decentralized trajectory optimization framework that incorporates the learned HJR solutions to solve MAMP tasks in real-time. We demonstrate that our method is both scalable and data-efficient, enabling the solution of MAMP problems in higher-dimensional scenarios with complex collision constraints. Our approach generalizes across various dynamical systems, including a 12-dimensional dual-arm setup, and outperforms a range of state-of-the-art techniques in successfully addressing challenging MAMP tasks. Video demonstrations are available at https://youtu.be/IZiePX0p1Mc.