In recent years, the area of Robot-Assisted Minimally Invasive Surgery (RAMIS) is standing on the the verge of a new wave of innovations. However, autonomy in RAMIS is still in a primitive stage. Therefore, most surge...
详细信息
ISBN:
(纸本)9798350384581;9798350384574
In recent years, the area of Robot-Assisted Minimally Invasive Surgery (RAMIS) is standing on the the verge of a new wave of innovations. However, autonomy in RAMIS is still in a primitive stage. Therefore, most surgeries still require manual control of the endoscope and the robotic instruments, resulting in surgeons needing to switch attention between performing surgical procedures and moving endoscope camera. automation may reduce the complexity of surgical operations and consequently reduce the cognitive load on the surgeon while speeding up the surgical process. In this paper, a hybrid robotic endoscope control system based on fusion model of natural language processing (NLP) and modified YOLO-V8 vision model is proposed. This proposed system can analyze the current surgical workflow and generate logs to summarize the procedure for teaching and providing feedback to junior surgeons. The user study of this system indicated a significant reduction of the number of clutching actions and mean task time, which effectively enhanced the surgical training.
Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons shou...
详细信息
ISBN:
(纸本)9798350384581;9798350384574
Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design.
Predicting the future trajectories of robots' nearby objects plays a pivotal role in applications such as autonomous driving. While learning-based trajectory prediction methods have achieved remarkable performance...
详细信息
ISBN:
(纸本)9798350323658
Predicting the future trajectories of robots' nearby objects plays a pivotal role in applications such as autonomous driving. While learning-based trajectory prediction methods have achieved remarkable performance on public benchmarks, the generalization ability of these approaches remains questionable. The poor generalizability on unseen domains, a well-recognized defect of data-driven approaches, can potentially harm the real-world performance of trajectory prediction models. We are thus motivated to improve models' generalization ability instead of merely pursuing high accuracy on average. Due to the lack of benchmarks for quantifying the generalization ability of trajectory predictors, we first construct a new benchmark called argoverse-shift, where the data distributions of domains are significantly different. Using this benchmark for evaluation, we identify that the domain shift problem seriously hinders the generalization of trajectory predictors since state-of-the-art approaches suffer from severe performance degradation when facing those out-of-distribution scenes. To enhance the robustness of models against domain shift problem, we propose a plug-and-play strategy for domain normalization in trajectory prediction. Our strategy utilizes the Frenet coordinate frame for modeling and can effectively narrow the domain gap of different scenes caused by the variety of road geometry and topology. Experiments show that our strategy noticeably boosts the prediction performance of the state-of-the-art in domains that were previously unseen to the models, thereby improving the generalization ability of data-driven trajectory prediction methods.
Large Language models (LLMs) have emerged as a new paradigm for embodied reasoning and control, most recently by generating robot policy code that utilizes a custom library of vision and control primitive skills. Howe...
详细信息
ISBN:
(纸本)9798350384581;9798350384574
Large Language models (LLMs) have emerged as a new paradigm for embodied reasoning and control, most recently by generating robot policy code that utilizes a custom library of vision and control primitive skills. However, prior arts fix their skills library and steer the LLM with carefully handcrafted prompt engineering, limiting the agent to a stationary range of addressable tasks. In this work, we introduce LRLL, an LLM-based lifelong learning agent that continuously grows the robot skill library to tackle manipulation tasks of ever-growing complexity. LRLL achieves this with four novel contributions: 1) a soft memory module that allows dynamic storage and retrieval of past experiences to serve as context, 2) a self-guided exploration policy that proposes new tasks in simulation, 3) a skill abstractor that distills recent experiences into new library skills, and 4) a lifelong learning algorithm for enabling human users to bootstrap new skills with minimal online interaction. LRLL continuously transfers knowledge from the memory to the library, building composable, general and interpretable policies, while bypassing gradient-based optimization, thus relieving the learner from catastrophic forgetting. Empirical evaluation in a simulated tabletop environment shows that LRLL outperforms end-to-end and vanilla LLM approaches in the lifelong setup while learning skills that are transferable to the real world. Project material will become available at the webpage https://***/LRLL_project/.
Active lower-limb prostheses could enable more natural assisted locomotion by contributing net positive work through important gait events, such as ankle push-off. This paper uses multi-contact models of locomotion to...
详细信息
ISBN:
(纸本)9798350323658
Active lower-limb prostheses could enable more natural assisted locomotion by contributing net positive work through important gait events, such as ankle push-off. This paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis. In particular, we leverage model-based control approaches for dynamic bipedal robotic walking to develop a systematic method to realize human-like walking on a powered prosthesis that does not require subject-specific tuning. The proposed controller is implemented on a prosthesis for 2 subjects without tuning between subjects, emulating subject-specific human kinematic trends on the prosthesis joints. These experimental results demonstrate that our forcebased nonlinear control approach achieves better tracking of human-like kinematic trajectories, with an average RMSE of 0.0223 during weight-bearing, compared to 2 non-force-sensing methods with an average RMSE of 0.0411 and 0.0430.
This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of lines-of-sight from a source point to all other grid cells in a single pas...
详细信息
ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581;9798350384574
This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of lines-of-sight from a source point to all other grid cells in a single pass with no preprocessing and independently of the number and shape of obstacles. It has a compute and memory complexity of O(n), where n = n(x) x n(y) is the size of the grid, and requires at most ten arithmetic operations per grid cell. In the proposed approach, we use a linear first-order hyperbolic partial differential equation to transport the visibility quantity in all directions. In order to accomplish that, we use an entropy-satisfying upwind scheme that converges to the true visibility polygon as the step size goes to zero. This dynamic-programming approach allows the evaluation of visibility for an entire grid orders of magnitude faster than typical ray-casting algorithms. We provide a practical application of our proposed algorithm by posing the visibility quantity as a heuristic and implementing a deterministic, local-minima-free path planner, setting apart the proposed planner from traditional methods. Lastly, we provide necessary algorithms and an open-source implementation of the proposed methods.
In the context of interaction with unmodelled systems, it becomes imperative for a robot controller to possess the capability to dynamically adjust its actions in real-time, enhancing its resilience in the face of flu...
详细信息
ISBN:
(纸本)9798350384581;9798350384574
In the context of interaction with unmodelled systems, it becomes imperative for a robot controller to possess the capability to dynamically adjust its actions in real-time, enhancing its resilience in the face of fluctuating environmental conditions. This adaptation process must be performed in a stability-preserving fashion, and resourcefully exploit the knowledge acquired during the interaction process. In this article, we propose a novel control strategy, based on the synergistic usage of state-of-the-art passivity-based control and Deep Reinforcement Learning (DRL). The concept of energy tank is used to provide stability guarantees for the interaction controller with uncertain environments, while an online learning policy allows to properly estimate the requirements of the task and adapt the controller accordingly, thus simultaneously achieving stability and performance. The proposed architecture is successfully validated through simulations and experiments with a collaborative manipulator in a surface polishing task.
In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, ther...
详细信息
ISBN:
(纸本)9798350384581;9798350384574
In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot's state and control spaces, thereby enabling a wider suite of dynamic tasks in real-world applications. Our contributions are three-fold: i) a method for discretizing the state and control spaces to amortize planning times across multiple queries;ii) lazy approaches to collision checking and propagation of control sequences that decrease the cost of physics-based simulation;and iii) LazyBoE, a robust kinodynamic planner that leverages these two contributions to produce dynamically-feasible trajectories. The proposed framework not only reduces planning time but also increases success rate in comparison to previous approaches.
This paper introduces a method for learning the force response of heterogeneous, deformable objects directly from robot sensor data without prior knowledge. The method estimates an object's force response given ro...
详细信息
ISBN:
(纸本)9798350323658
This paper introduces a method for learning the force response of heterogeneous, deformable objects directly from robot sensor data without prior knowledge. The method estimates an object's force response given robot force or torque measurements using a novel volumetric stiffness field representation and point-based contact simulator. The stiffness of each point colliding with the robot is estimated independently and is updated upon each observed measurement using a projected diagonal Kalman filter. Experiments show that this method can update a stiffness field over 105 points at 23 Hz or higher, and is more accurate than learning-based methods in predicting torque response while touching artificial plants. The method can also be augmented with visual information to help extrapolate stiffness fields to distant parts of the touched object using only a small number of touches.
Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly...
详细信息
ISBN:
(纸本)9798350323658
Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory Control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collaboration system.
暂无评论