Continuum robot has good motion flexibility and strong environmental compliance. It can well adapt to large deformation and complex environment, and can carry out safe, dexterous and accurate intelligent interaction b...
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ISBN:
(纸本)9798350385731;9798350385724
Continuum robot has good motion flexibility and strong environmental compliance. It can well adapt to large deformation and complex environment, and can carry out safe, dexterous and accurate intelligent interaction between robot and environment. Because of the above advantages, continuum robot can be widely used in medical equipment, industrial production and other fields. The realization of safe, stable and flexible intelligent interactive operation of continuum robot in constrained space is the focus and difficulty in these fields. The research status of continuum robot system is investigated and analyzed in aspects of model analysis, trajectory planning and control methods in this manuscript, so as to provide a useful reference for scholars related to continuum robot.
Non-prehensile transportation of unstable objects presents a challenging task in robotics. To ensure the success of the transportation, it is necessary to consider both the object's stability via contact dynamics ...
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ISBN:
(纸本)9798350377712;9798350377705
Non-prehensile transportation of unstable objects presents a challenging task in robotics. To ensure the success of the transportation, it is necessary to consider both the object's stability via contact dynamics and the motion constraints of the robot. We propose two novel trajectory planning methods derived from sampling and dynamic programming algorithms, tested on a 7-DoF Franka Emika robot against common strategies like Model Predictive Control (MPC) and S-curve planning, particularly under the constraint of a non-rotating tray. The results demonstrate the effectiveness of our methodologies in improving transportation speed. This research contributes to advancements in robotic manipulation techniques by tackling non-prehensile manipulation of dynamically unstable objects.
This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus on following complex instructions th...
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ISBN:
(纸本)9798350384581;9798350384574
This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus on following complex instructions that are more akin to natural conversation than traditional explicit procedural directives typically seen in robotics. Unlike most prior work where navigation directives are provided as simple imperative commands (e.g., "go to the fridge"), we examine implicit directives obtained through conversational *** leverage the 3D simulator AI2Thor to create household query scenarios at scale, and augment it by adding complex language queries for 40 object types. We demonstrate that a robot using our method CARTIER (Cartographic lAnguage Reasoning Targeted at Instruction Execution for robots) can parse descriptive language queries up to 42% more reliably than existing LLM-enabled methods by exploiting the ability of LLMs to interpret the user interaction in the context of the objects in the scenario.
This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on...
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ISBN:
(纸本)9798350377712;9798350377705
This article studies the commonsense object affordance concept for enabling close-to-human task planning and task optimization of embodied robotic agents in urban environments. The focus of the object affordance is on reasoning how to effectively identify object's inherent utility during the task execution, which in this work is enabled through the analysis of contextual relations of sparse information of 3D scene graphs. The proposed framework develops a Correlation Information (CECI) model to learn probability distributions using a Graph Convolutional Network, allowing to extract the commonsense affordance for individual members of a semantic class. The overall framework was experimentally validated in a real-world indoor environment, showcasing the ability of the method to level with human commonsense. For a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/BDCMVx2GiQE
In mobile robotics, particularly in autonomous driving, localization is one of the key challenges for navigation and planning. For safe operation in the open world where vulnerable participants are present, precise an...
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ISBN:
(纸本)9798350377712;9798350377705
In mobile robotics, particularly in autonomous driving, localization is one of the key challenges for navigation and planning. For safe operation in the open world where vulnerable participants are present, precise and guaranteed safe localization is required. While current classical fusion approaches are safe due to provably bounded closed-form formulation, their situation-adaptivity is limited. In contrast, data-driven approaches are situation-adaptive based on the underlying training data but unbounded and unsafe. In our work, we propose a novel data-driven but provably bounded sensor fusion and apply it to mobile robotic localization. In extensive experiments using an autonomous driving test vehicle, we show that our fusion method outperforms other safe fusion approaches.
To give the single robot system the ability to realize many behaviors and to realize tasks with shifting environments and objectives, it is necessary to abstract the robot's body ability to the extent that they ca...
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ISBN:
(纸本)9798350377712;9798350377705
To give the single robot system the ability to realize many behaviors and to realize tasks with shifting environments and objectives, it is necessary to abstract the robot's body ability to the extent that they can be detected by sensors in the body so that we can plan as the problem of state transition. In this paper, to abstract the transformer robot system that performs heavy lifting and environmental attachment tasks in an aquatic and terrestrial environment, we extend the graphical representation of the robot's body to manage joint capability and the body adaptability for the environment. To abstract the body ability, we divide the body into elements and define Connection between them at three different granularities. And using Connection, we propose the Connection Modification Feature(CMF) as the representation for changing body ability. To implement the Connection Modification Feature, we perform the abstract description and extract Connection to construct Body Ability Graph, a graph for the robot to manage its body ability. We show that it is possible to plan to manipulate and use its own Connection Modification Feature through multiple experiments by defining Normal Action that does not change body abilities and Body Ability Modifying Action that manipulate body abilities.
Recent advances in Large Language Models (LLMs) have showcased their remarkable reasoning capabilities, making them influential across various fields. However, in robotics, their use has primarily been limited to mani...
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ISBN:
(纸本)9798350377712;9798350377705
Recent advances in Large Language Models (LLMs) have showcased their remarkable reasoning capabilities, making them influential across various fields. However, in robotics, their use has primarily been limited to manipulation planning tasks due to their inherent textual output. This paper addresses this limitation by investigating the potential of adopting the reasoning ability of LLMs for generating numerical predictions in robotics tasks, specifically for robotic grasping. We propose Reasoning Tuning, a novel method that integrates a reasoning phase before prediction during training, leveraging the extensive prior knowledge and advanced reasoning abilities of LLMs. This approach enables LLMs, notably with multi-modal capabilities, to generate accurate numerical outputs like grasp poses that are context-aware and adaptable through conversations. Additionally, we present the Reasoning Tuning VLM Grasp dataset, carefully curated to facilitate the adaptation of LLMs to robotic grasping. Extensive validation on both grasping datasets and real-world experiments underscores the adaptability of multi-modal LLMs for numerical prediction tasks in robotics. This not only expands their applicability but also bridges the gap between text-based planning and direct robot control, thereby maximizing the potential of LLMs in robotics. Our dataset will be released. More details and videos of this work are available on our project page: https://***/view/rt-grasp.
Current methods based on Neural Radiance Fields (NeRF) significantly lack the capacity to quantify uncertainty in their predictions, particularly on the unseen space including the occluded and outside scene content. T...
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ISBN:
(纸本)9798350384581;9798350384574
Current methods based on Neural Radiance Fields (NeRF) significantly lack the capacity to quantify uncertainty in their predictions, particularly on the unseen space including the occluded and outside scene content. This limitation hinders their extensive applications in robotics, where the reliability of model predictions has to be considered for tasks such as robotic exploration and planning in unknown environments. To address this, we propose a novel approach to estimate a 3D Uncertainty Field based on the learned incomplete scene geometry, which explicitly identifies these unseen regions. By considering the accumulated transmittance along each camera ray, our Uncertainty Field infers 2D pixel-wise uncertainty, exhibiting high values for rays directly casting towards occluded or outside the scene content. To quantify the uncertainty on the learned surface, we model a stochastic radiance field. Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels, compared with recent methods. Furthermore, we illustrate that our designed uncertainty field is ideally suited for real-world robotics tasks, such as next-best-view selection.
Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent ...
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ISBN:
(纸本)1577358872
Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.
In recent years, we have seen tremendous progress in the field of legged robotics and the application of quadrupedal systems in real-world scenarios. Besides the massive improvement of the hardware systems to rugged a...
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