A linearized solver estimates rolling-shutter relative pose and motion from 7 affine correspondences in 1.2 ms and reports best-in-benchmark accuracy plus usable translational velocity.
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ALTRO: A Fast Solver for Constrained Trajectory Optimization
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Provides a layered algebraic quantitative semantics for STL-GO where soundness and completeness reduce to monotonicity of abstract accumulators, demonstrated via simulations on Dubins-car and satellite systems under four instantiations.
Formalizes CT-TAPF problem and introduces CT-TCBS optimal solver using incremental expansion for team formation plus task-centric sub-optimal solvers that improve efficiency over agent-centric baselines.
A smooth exponential obstacle cost with reduction factor in nonlinear MPC allows morphing quadrotors to traverse narrow gaps under limited 2D LiDAR perception.
A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows.
Introduces a stochastic DDP algorithm that optimizes nominal controls and feedback gains for belief-state trajectory problems under partial observability without relying on the separation principle.
SODA uses differential algebra and adaptive Gaussian mixtures to solve chance-constrained nonlinear trajectory optimization problems for space missions with non-Gaussian uncertainties.
RT-H learns robot policies by first predicting language motions as an intermediate representation and then mapping those plus the high-level task to actions, yielding more robust multi-task performance and the ability to learn from language interventions.
A per-joint 2-DoF feedforward–feedback controller with disturbance observer shapes real actuators to match idealized second-order sim dynamics, enabling zero-shot RL policy transfer.
Grasp pretraining on 355k trajectories improves full-task success on six articulated tool-use tasks by 33.3 pp over DP3 in real-world experiments.
TAP-VLA improves VLA performance in contact-rich manipulation by visually annotating tactile shear fields onto input images, reaching 78% success versus under 50% for vision-only and other tactile methods.
FWAV-Sim is a high-fidelity Unity simulation framework for flapping-wing vehicles that integrates blade-element aerodynamics with bluff-body drag, spatiotemporally correlated fractal turbulence, and realistic IMU/LiDAR/RGB sensor models to support autonomy development.
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
frax is a new open-source JAX library delivering low-microsecond CPU dynamics and over 100 million GPU evaluations per second for robot kinematics and dynamics with autodiff support.
cuRoboV2 unifies B-spline optimization, GPU-native dense signed distance fields, and scalable whole-body kinematics and dynamics to achieve 99.7% success on payloaded manipulators and 99.6% collision-free IK on 48-DoF humanoids.
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
DADDy combines differential dynamic programming with differential algebra to accelerate constrained fuel-optimal low-thrust trajectory optimization, reporting 41-88% runtime reductions on Sun-centred, Earth-Moon and Earth-centred benchmarks while retaining convergence.
Hierarchical DRL enables AUVs to navigate obstacles using raw camera and sonar data, performing within 4-6% of RRT* in simulation.
CLASP combines TP-KMPs with VLMs for language-guided skill selection, covariance-weighted composition, and active learning requests, reporting 73.3-100% success on a 7-DoF manipulator.
Hybrid ME-DDP variants combine deterministic DDP with inverse-Hessian sampling to improve success rates over pure DDP and MPPI in robotic navigation under non-convex costs.
Survey organizing world models for robotic manipulation into representation families, a functional taxonomy, and infrastructure roles across pretraining, post-training, and inference, while reviewing 34 datasets and evaluation protocols.
Systematic grasping strategies for paper-like materials are developed and tested with a soft gripper by exploiting environmental constraints to improve force control and success rates.
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
citing papers explorer
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Rolling Shutter Relative Pose Estimation Made Practical
A linearized solver estimates rolling-shutter relative pose and motion from 7 affine correspondences in 1.2 ms and reports best-in-benchmark accuracy plus usable translational velocity.
-
An Algebraic Framework for Quantitative Semantics of Spatio-Temporal Logic with Graph Operators
Provides a layered algebraic quantitative semantics for STL-GO where soundness and completeness reduce to monotonicity of abstract accumulators, demonstrated via simulations on Dubins-car and satellite systems under four instantiations.
-
Multi-Agent Cooperative Transportation: Optimal and Efficient Task Allocation and Path Finding
Formalizes CT-TAPF problem and introduces CT-TCBS optimal solver using incremental expansion for team formation plus task-centric sub-optimal solvers that improve efficiency over agent-centric baselines.
-
Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception
A smooth exponential obstacle cost with reduction factor in nonlinear MPC allows morphing quadrotors to traverse narrow gaps under limited 2D LiDAR perception.
-
Asymptotically Optimal Ergodic Coverage on Generalized Motion Fields
A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows.
-
Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability
Introduces a stochastic DDP algorithm that optimizes nominal controls and feedback gains for belief-state trajectory problems under partial observability without relying on the separation principle.
-
Non-linear stochastic trajectory optimisation
SODA uses differential algebra and adaptive Gaussian mixtures to solve chance-constrained nonlinear trajectory optimization problems for space missions with non-Gaussian uncertainties.
-
RT-H: Action Hierarchies Using Language
RT-H learns robot policies by first predicting language motions as an intermediate representation and then mapping those plus the high-level task to actions, yielding more robust multi-task performance and the ability to learn from language interventions.
-
Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning
A per-joint 2-DoF feedforward–feedback controller with disturbance observer shapes real actuators to match idealized second-order sim dynamics, enabling zero-shot RL policy transfer.
-
From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation
Grasp pretraining on 355k trajectories improves full-task success on six articulated tool-use tasks by 33.3 pp over DP3 in real-world experiments.
-
TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models
TAP-VLA improves VLA performance in contact-rich manipulation by visually annotating tactile shear fields onto input images, reaching 78% success versus under 50% for vision-only and other tactile methods.
-
A Simulation Platform for Flapping-Wing Vehicles
FWAV-Sim is a high-fidelity Unity simulation framework for flapping-wing vehicles that integrates blade-element aerodynamics with bluff-body drag, spatiotemporally correlated fractal turbulence, and realistic IMU/LiDAR/RGB sensor models to support autonomy development.
-
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
-
frax: Fast Robot Kinematics and Dynamics in JAX
frax is a new open-source JAX library delivering low-microsecond CPU dynamics and over 100 million GPU evaluations per second for robot kinematics and dynamics with autodiff support.
-
cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots
cuRoboV2 unifies B-spline optimization, GPU-native dense signed distance fields, and scalable whole-body kinematics and dynamics to achieve 99.7% success on payloaded manipulators and 99.6% collision-free IK on 48-DoF humanoids.
-
Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
-
Taylor polynomial-based constrained solver for fuel-optimal low-thrust trajectory optimisation
DADDy combines differential dynamic programming with differential algebra to accelerate constrained fuel-optimal low-thrust trajectory optimization, reporting 41-88% runtime reductions on Sun-centred, Earth-Moon and Earth-centred benchmarks while retaining convergence.
-
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
Hierarchical DRL enables AUVs to navigate obstacles using raw camera and sonar data, performing within 4-6% of RRT* in simulation.
-
CLASP: Language-Driven Robot Skill Selection and Composition using Task-Parameterized Learning
CLASP combines TP-KMPs with VLMs for language-guided skill selection, covariance-weighted composition, and active learning requests, reporting 73.3-100% success on a 7-DoF manipulator.
-
Beyond Pure Sampling: Hybrid Optimization Mechanisms for Non-Convex Model Predictive Control
Hybrid ME-DDP variants combine deterministic DDP with inverse-Hessian sampling to improve success rates over pure DDP and MPPI in robotic navigation under non-convex costs.
-
World Models for Robotic Manipulation: A Survey
Survey organizing world models for robotic manipulation into representation families, a functional taxonomy, and infrastructure roles across pretraining, post-training, and inference, while reviewing 34 datasets and evaluation protocols.
-
Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper
Systematic grasping strategies for paper-like materials are developed and tested with a soft gripper by exploiting environmental constraints to improve force control and success rates.
-
Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
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From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
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Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards
Integrating foot position maps into heightmaps and adding a locomotion-stability reward in an attention-based RL framework improves quadrupedal success rates on both trained and out-of-domain complex terrains.
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STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle
The paper proposes an STL-based optimization planner with uncertainty-aware risk analysis and event-triggered replanning for safe human-drone collaboration, demonstrated in simulations of an object handover task.
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Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots
Soft growing robots map unknown 2D environments by characterizing collision deformations, building a geometry-based simulator, and using Monte Carlo sampling to select optimal deployments that approach ideal actions.
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A Multi-Agent system for Multi-Objective constrained optimization
MAMO uses multi-agent RL to automatically select reward weights for constrained optimization problems in non-stationary environments.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation
A neuromorphic spiking ring attractor maintains stable multi-second representations of robot joint angles with reduced drift near limits and a near-linear velocity-to-bump-speed relationship.
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Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
The paper formulates single-UAV medical delivery trajectory planning as a convex optimization problem that incorporates signal temporal logic for temporal constraints and convex feasible set collision avoidance for urban obstacles.
- SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling