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.
hub Mixed citations
ALTRO: A Fast Solver for Constrained Trajectory Optimization
Mixed citation behavior. Most common role is background (50%).
32
Pith papers citing it
Background
50% of classified citations
hub tools
citation-role summary
background 4
baseline 1
method 1
citation-polarity summary
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
No citing papers match the current filters.