A Relational Approach to Tool-Use Learning in Robots

Brown, Solly; Sammut, Claude

doi:10.1007/978-3-642-38812-5_1

Cited by 17 publications

(25 citation statements)

References 52 publications

(95 reference statements)

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“…All of these systems use feature vector representations to describe an object's attributes, and therefore, are limited in their ability to describe relations between components of the tool and relations to other objects. Relational learning methods overcome those limitations [1].…”

Section: Related Workmentioning

confidence: 99%

“…TRAIL [13] was the first to attempt at learning action models in a noisy world using ILP. Brown & Sammut [1] applied Explanation-based Learning (EBL) and ILP to learn an action model of a simulated robot that uses tools to achieve its goals.…”

Section: Related Workmentioning

confidence: 99%

“…hs is refined by finding the constrained Least General Generalisation (LGG) of a pair of positive examples, and hg is refined by performing a mechanism inspired by Negative-based Reduction (NBR). The tool use learning algorithm, extends Brown & Sammut [1] and Haber [22], is shown in tool use learning algorithm in Figure 2.…”

Section: Tool Use Learningmentioning

confidence: 99%

“…When an appropriate tool is not available, humans can innovate and design a new tool, often based on prior experience, or even invent it from scratch. In previous work, Brown & Sammut [1] showed how a robot can learn to use an existing object as a tool. However, tool creation is a much more difficult task.…”

Section: Introductionmentioning

confidence: 99%

“…A constraint solver can then be used to provide bounds on the numerical parameters required to manufacture the object by 3D printing. Unlike Brown & Sammut [1] who performed experiments in a sensor-less simulation, we conduct our experiments using a real Baxter robot, and for testing on large numbers of example, we use a sensor-based simulation of the Baxter. In the following sections, we review related work in tool creation by a robot, describe our representation of states and action, and explain our learning mechanism.…”

Section: Introductionmentioning

confidence: 99%

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A cognitive robot equipped with autonomous tool innovation expertise

Wicaksono¹,

Sammut²

2020

IJECE

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Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Tool Use Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A cognitive robot equipped with autonomous tool innovation expertise

Wicaksono¹,

Sammut²

2020

IJECE

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A Review of Inductive Logic Programming Applications for Robotic Systems

Youssef,

Müller

2023

Lecture Notes in Computer Science

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Relational affordances for multiple-object manipulation

et al. 2017

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The concept of affordances has been used in robotics to model action opportunities of a robot and as a basis for making decisions involving objects. Affordances capture the interdependencies between the objects and their properties, the executed actions on those objects, and the effects of those respective actions. However, existing affordance models cannot cope with multiple objects that may interact during action execution. Our approach is unique in that possesses the following four characteristics. First, our model employs recent advances in probabilistic programming to learn affordance models that take into account (spatial) relations between different objects, such as relative distances. Two-object interaction models are first learned from the robot interacting with the world in a behavioural exploration stage, and are then employed in worlds with an arbitrary number of objects. The model thus generalizes over both the number of and the particular objects used in the exploration stage, and it also effectively deals with uncertainty. Secondly, rather than using a (discrete) action repertoire, the actions are parametrised according to the motor capabilities of the robot, which allows to model and achieve goals at several levels of complexity. It also supports a two-arm parametrised action. Thirdly, the relational affordance model represents the state of the world using both discrete (action and object features) and continuous (effects) random variables. The effects follow a multivariate Gaussian distribution with the correlated discrete variables (actions and object properties). Fourthly,

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A Relational Approach to Tool-Use Learning in Robots

Cited by 17 publications

References 52 publications

A cognitive robot equipped with autonomous tool innovation expertise

A cognitive robot equipped with autonomous tool innovation expertise

A Review of Inductive Logic Programming Applications for Robotic Systems

Relational affordances for multiple-object manipulation

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