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Performing complex manipulation tasks in dynamic environments requires efficient Task and Motion Planning (TAMP) approaches, which combine high-level symbolic plan with low-level motion planning. Advances in Large Language Models (LLMs), such as GPT-4, are transforming task planning by offering natural language as an intuitive and flexible way to describe tasks, generate symbolic plans, and reason. However, the effectiveness of LLM-based TAMP approaches is limited due to static and template-based prompting, which struggles in adapting to dynamic environments and complex task contexts. To address these limitations, this work proposes a novel ontology-driven prompt-tuning framework that employs knowledge-based reasoning to refine and expand user prompts with task contextual reasoning and knowledge-based environment state descriptions. Integrating domain-specific knowledge into the prompt ensures semantically accurate and context-aware task plans. The proposed framework demonstrates its effectiveness by resolving semantic errors in symbolic plan generation, such as maintaining logical temporal goal ordering in scenarios involving hierarchical object placement. The proposed framework is validated through both simulation and real-world scenarios, demonstrating significant improvements over the baseline approach in terms of adaptability to dynamic environments, and the generation of semantically correct task plans., Comment: Submitted to Robotics and Automation Letters

Generating context-adaptive manipulation and grasping actions is a challenging problem in robotics. Classical planning and control algorithms tend to be inflexible with regard to parameterization by external variables such as object shapes. In contrast, Learning from Demonstration (LfD) approaches, due to their nature as function approximators, allow for introducing external variables to modulate policies in response to the environment. In this paper, we utilize this property by introducing an LfD approach to acquire context-dependent grasping and manipulation strategies. We treat the problem as a kernel-based function approximation, where the kernel inputs include generic context variables describing task-dependent parameters such as the object shape. We build on existing work on policy fusion with uncertainty quantification to propose a state-dependent approach that automatically returns to demonstrations, avoiding unpredictable behavior while smoothly adapting to context changes. The approach is evaluated against the LASA handwriting dataset and on a real 7-DoF robot in two scenarios: adaptation to slippage while grasping and manipulating a deformable food item.

One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the difficulty lies in adapting the grasping strategies to a large variety of tasks and objects, which can often be unknown. The brute-force solution is to learn new grasps by trial and error, which is inefficient and ineffective. In contrast, Bayesian optimization applies active learning by adding information to the approximation of an optimal grasp. This paper proposes the use of Bayesian optimization techniques to safely perform robotic grasping. We analyze different grasp metrics to provide realistic grasp optimization in a real system including tactile sensors. An experimental evaluation in the robotic system shows the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent to a real-world environment.

Inherent robustness in manipulation is prevalent in biological systems and critical for robotic manipulation systems due to real-world uncertainties and disturbances. This robustness relies not only on robust control policies but also on the design characteristics of the end-effectors. This paper introduces a bi-level optimization approach to co-designing tools and control policies to achieve robust manipulation. The approach employs reinforcement learning for lower-level control policy learning and multi-task Bayesian optimization for upper-level design optimization. Diverging from prior approaches, we incorporate caging-based robustness metrics into both levels, ensuring manipulation robustness against disturbances and environmental variations. Our method is evaluated in four non-prehensile manipulation environments, demonstrating improvements in task success rate under disturbances and environment changes. A real-world experiment is also conducted to validate the framework's practical effectiveness.

This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research directions for the robotic manipulation domain., Comment: Accepted for publication by IEEE Robotics and Automation Letters

In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, specifically table organization tasks. We provide a set of identical real robot setups and facilitate remote experiments of standardized table organization scenarios in varying difficulties. In this workflow, users upload their solutions to our remote server and their code is executed on the real robot setups and scored automatically. After each execution, the OCRTOC team resets the experimental setup manually. We also provide a simulation environment that researchers can use to develop and test their solutions. With the OCRTOC benchmark, we aim to lower the barrier of conducting reproducible research on robotic grasping and manipulation and accelerate progress in this field. Executing standardized scenarios on identical real robot setups allows us to quantify algorithm performances and achieve fair comparisons. Using this benchmark we held a competition in the 2020 International Conference on Intelligence Robots and Systems (IROS 2020). In total, 59 teams took part in this competition worldwide. We present the results and our observations of the 2020 competition, and discuss our adjustments and improvements for the upcoming OCRTOC 2021 competition. The homepage of the OCRTOC competition is www.ocrtoc.org, and the OCRTOC software package is available at https://github.com/OCRTOC/OCRTOC_software_package.

Humans use environmental constraints (EC) in manipulation to compensate for uncertainties in their world model. The same principle was recently applied to robotics, so that soft underactuated hands improve their grasping capability by using environmental constraints exploitation (ECE) [1]. Due to orientation of the robotic hand for example in the EC wall grasp, the length of the robot wrist plus the hand length gets quite important, if objects are grasp out of a box [2] . Most of the modern cobots have quite long wrist, so we have constructed a two degree of freedom wrist for the CLASH [3], to solve this problem (Fig. 1)., Comment: IROS 2019, Factory of the Future