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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representative citing papers
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.
SA-LIVO uses eigendecomposition of the joint information matrix with linear-clamp soft gates per eigendirection for efficient degeneracy-aware LiDAR-inertial-visual odometry.
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.
Actuator reality shaping uses a 2DOF controller to align real actuator closed-loop behavior with idealized simulation reference dynamics, enabling zero-shot sim-to-real policy deployment across multiple robot platforms.
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.
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.
-
SA-LIVO: Efficient LiDAR-Inertial-Visual Odometry with Subspace-Aware Degeneracy Handling
SA-LIVO uses eigendecomposition of the joint information matrix with linear-clamp soft gates per eigendirection for efficient degeneracy-aware LiDAR-inertial-visual odometry.
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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.
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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
Actuator reality shaping uses a 2DOF controller to align real actuator closed-loop behavior with idealized simulation reference dynamics, enabling zero-shot sim-to-real policy deployment across multiple robot platforms.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.