NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
11 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users.
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ASCII rendering of visual states enables fine-tuned text-only LLMs to serve as VLA controllers that identify objects and generate feasible action sequences in 2D manipulation benchmarks in simulation and on hardware.
AffordanceVLA proposes a VLA model with affordance-aware modules (Which2Act, Where2Act, How2Act) in a Mixture-of-Transformer trained in three stages to improve robotic manipulation.
WorkBenchMark is a new LEGO-based benchmark for robotic assembly tasks with an Assembly-by-Disassembly baseline that outperforms vision-language-action methods across all tiers.
Introduces a directed graphical abstraction for topple actions in stack rearrangement, reducing planning to a pebble motion variant and showing faster execution in simulation than pick-and-place alone.
3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.
MagicSim is a unified embodied interaction infrastructure built on a deterministic batched runtime and shared MDP that supports diverse world construction, execution, task evaluation, automatic rollout generation, and interactive agent interfaces.
TouchDrive delivers tactile cues for grasp force modulation in assistive robotics using only a pneumatic normally closed valve, compressed air tank, sensing element, and haptic actuator in a single passive mechanical loop.
Hybrid TAMP framework with GMM-informed RRT, MoveIt/FCL predictive checking, and reactive vision avoidance reduces path length 63% and swept volumes in real dual-robot EV battery disassembly.
ROS-LLM integrates LLMs with ROS to let non-experts specify robot tasks in natural language, supporting sequence, behavior tree, and state machine modes plus imitation learning and reflection on feedback.
A middleware interface employs n-degree polynomial interpolation and quadratic programming to produce smooth, real-time end-effector trajectories for collaborative arms, validated in offline drawing, dynamic grasping, and teleoperation experiments.
citing papers explorer
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NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
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ASCII Art Turns LLMs into VLA Controllers
ASCII rendering of visual states enables fine-tuned text-only LLMs to serve as VLA controllers that identify objects and generate feasible action sequences in 2D manipulation benchmarks in simulation and on hardware.
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AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding
AffordanceVLA proposes a VLA model with affordance-aware modules (Which2Act, Where2Act, How2Act) in a Mixture-of-Transformer trained in three stages to improve robotic manipulation.
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WorkBenchMark: A LEGO-Based Assembly Benchmark with an Assembly-by-Disassembly Baseline for the Smart Manufacturing League
WorkBenchMark is a new LEGO-based benchmark for robotic assembly tasks with an Assembly-by-Disassembly baseline that outperforms vision-language-action methods across all tiers.
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Virtues of Ordered Chaos: Planning with Topple Actions in Tabletop Stack Rearrangement
Introduces a directed graphical abstraction for topple actions in stack rearrangement, reducing planning to a pebble motion variant and showing faster execution in simulation than pick-and-place alone.
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3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.
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MagicSim: A Unified Infrastructure for Executable Embodied Interaction
MagicSim is a unified embodied interaction infrastructure built on a deterministic batched runtime and shared MDP that supports diverse world construction, execution, task evaluation, automatic rollout generation, and interactive agent interfaces.
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TouchDrive: Electronics-Free Tactile Sensing Interface for Assistive Grasping
TouchDrive delivers tactile cues for grasp force modulation in assistive robotics using only a pneumatic normally closed valve, compressed air tank, sensing element, and haptic actuator in a single passive mechanical loop.
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Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries
Hybrid TAMP framework with GMM-informed RRT, MoveIt/FCL predictive checking, and reactive vision avoidance reduces path length 63% and swept volumes in real dual-robot EV battery disassembly.
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ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
ROS-LLM integrates LLMs with ROS to let non-experts specify robot tasks in natural language, supporting sequence, behavior tree, and state machine modes plus imitation learning and reflection on feedback.
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One Interface, Many Robots: Unified Real-Time Low-Level Motion Planning for Collaborative Arms
A middleware interface employs n-degree polynomial interpolation and quadratic programming to produce smooth, real-time end-effector trajectories for collaborative arms, validated in offline drawing, dynamic grasping, and teleoperation experiments.