Learning grasp affordance reasoning through semantic relations

Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction.Comment: Accepted in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 201

Affordance-Aware Handovers With Human Arm Mobility Constraints

Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities. While there are existing approaches for estimating the effectiveness of handovers, their findings are limited to users without arm mobility impairments and to specific objects. Therefore, current state-of-the-art approaches are unable to hand over novel objects to receivers with different arm mobility capacities. We propose a method that generalises handover behaviours to previously unseen objects, subject to the constraint of a user's arm mobility levels and the task context. We propose a heuristic-guided hierarchically optimised cost whose optimisation adapts object configurations for receivers with low arm mobility. This also ensures that the robot grasps consider the context of the user's upcoming task, i.e., the usage of the object. To understand preferences over handover configurations, we report on the findings of an online study, wherein we presented different handover methods, including ours, to $259$ users with different levels of arm mobility. We find that people's preferences over handover methods are correlated to their arm mobility capacities. We encapsulate these preferences in a statistical relational model (SRL) that is able to reason about the most suitable handover configuration given a receiver's arm mobility and upcoming task. Using our SRL model, we obtained an average handover accuracy of $90.8\%$ when generalising handovers to novel objects.Comment: Accepted for RA-L 202

GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-world concept sets, restraining the generalization to novel concepts out of the pre-defined sets. To address this issue, we propose GraspGPT, a large language model (LLM) based TOG framework that leverages the open-end semantic knowledge from an LLM to achieve zero-shot generalization to novel concepts. We conduct experiments on Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that GraspGPT outperforms existing TOG methods on different held-out settings when generalizing to novel concepts out of the training set. The effectiveness of GraspGPT is further validated in real-robot experiments. Our code, data, appendix, and video are publicly available at https://sites.google.com/view/graspgpt/.Comment: 15 pages, 8 figure

Affordance-Based Human-Robot Interaction With Reinforcement Learning

Planning precise manipulation in robotics to perform grasp and release-related operations, while interacting with humans is a challenging problem. Reinforcement learning (RL) has the potential to make robots attain this capability. In this paper, we propose an affordance-based human-robot interaction (HRI) framework, aiming to reduce the action space size that would considerably impede the exploration efficiency of the agent. The framework is based on a new algorithm called Contextual Q-learning (CQL). We first show that the proposed algorithm trains in a reduced amount of time (2.7 seconds) and reaches an 84% of success rate. This suits the robot’s learning efficiency to observe the current scenario configuration and learn to solve it. Then, we empirically validate the framework for implementation in HRI real-world scenarios. During the HRI, the robot uses semantic information from the state and the optimal policy of the last training step to search for relevant changes in the environment that may trigger the generation of a new policy

Reasoning and understanding grasp affordances for robot manipulation

This doctoral research focuses on developing new methods that enable an artificial agent to grasp and manipulate objects autonomously. More specifically, we are using the concept of affordances to learn and generalise robot grasping and manipulation techniques. [75] defined affordances as the ability of an agent to perform a certain action with an object in a given environment. In robotics, affordances defines the possibility of an agent to perform actions with an object. Therefore, by understanding the relation between actions, objects and the effect of these actions, the agent understands the task at hand, providing the robot with the potential to bridge perception to action. The significance of affordances in robotics has been studied from varied perspectives, such as psychology and cognitive sciences. Many efforts have been made to pragmatically employ the concept of affordances as it provides the potential for an artificial agent to perform tasks autonomously. We start by reviewing and finding common ground amongst different strategies that use affordances for robotic tasks. We build on the identified grounds to provide guidance on including the concept of affordances as a medium to boost autonomy for an artificial agent. To this end, we outline common design choices to build an affordance relation; and their implications on the generalisation capabilities of the agent when facing previously unseen scenarios. Based on our exhaustive review, we conclude that prior research on object affordance detection is effective, however, among others, it has the following technical gaps: (i) the methods are limited to a single object ↔ affordance hypothesis, and (ii) they cannot guarantee task completion or any level of performance for the manipulation task alone nor (iii) in collaboration with other agents. In this research thesis, we propose solutions to these technical challenges. In an incremental fashion, we start by addressing the limited generalisation capabilities of, at the time state-of-the-art methods, by strengthening the perception to action connection through the construction of an Knowledge Base (KB). We then leverage the information encapsulated in the KB to design and implement a reasoning and understanding method based on statistical relational leaner (SRL) that allows us to cope with uncertainty in testing environments, and thus, improve generalisation capabilities in affordance-aware manipulation tasks. The KB in conjunctions with our SRL are the base for our designed solutions that guarantee task completion when the robot is performing a task alone as well as when in collaboration with other agents. We finally expose and discuss a range of interesting avenues that have the potential to thrive the capabilities of a robotic agent through the use of the concept of affordances for manipulation tasks. A summary of the contributions of this thesis can be found at: https://bit.ly/grasp_affordance_reasonin

Graph learning in robotics: a survey

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applications. To fully unlock their potential, hence, we propose a review of graph neural architectures from a robotics perspective. The paper covers the fundamentals of graph-based models, including their architecture, training procedures, and applications. It also discusses recent advancements and challenges that arise in applied settings, related for example to the integration of perception, decision-making, and control. Finally, the paper provides an extensive review of various robotic applications that benefit from learning on graph structures, such as bodies and contacts modelling, robotic manipulation, action recognition, fleet motion planning, and many more. This survey aims to provide readers with a thorough understanding of the capabilities and limitations of graph neural architectures in robotics, and to highlight potential avenues for future research

Behaviour-driven motion synthesis

Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as well as to hazardous environments, has led to an increased interest in real-world deployment of robotic agents. Targeted applications require robots to be adept at synthesising complex motions rapidly across a wide range of tasks and environments. To this end, this thesis proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and integrate them tightly into the motion synthesis process to make real-world deployment in complex environments practical. We investigate three principal challenges of this proposition. Firstly, we argue that behavioural samples in form of trajectories are of particular interest to guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation that enables the storage and bootstrap of sets of problem-relevant trajectories. Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task instantiations that differ significantly from those stored in the framework. We present two novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration based on trajectory segmentation and malleability. Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We propose a multi-layer planner that first solves a simplified version of the problem at hand, to then inform the search for a solution in the constrained space. Our contributions on efficient motion synthesis via behaviour guidance augment the robots’ capabilities to deal with more complex planning problems, and do so more effectively than related approaches in the literature by computing better quality paths in lower response time. We demonstrate our contributions, in both laboratory experiments and field trials, on a spectrum of planning problems and robotic platforms ranging from high-dimensional humanoids and robotic arms with a focus on autonomous manipulation in resembling environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion synthesis, we have explored the applicability of our findings on disparate robotic fields, such as grasp and task planning. We have made some of our contributions open-source hoping they will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science Alliance (SICSA