| 1. |
- Hellström, Thomas, 1956-, et al.
(författare)
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A Java-based middleware for control and sensing in mobile robotics
- 2008
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Ingår i: International Conference on Intelligent Automation and Robotics 2008. - 9789889867102 ; , s. 649-654
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Konferensbidrag (refereegranskat)abstract
- Many of the existing mobile-robot software packages do not include handling of sensors and actuators in a sufficiently systematic and uniform way, as described later in this section. The software framework proposed in this paper, denoted NAV2000, addresses the specific need for interchangeability of components in robotics. At the lowest level, sensors, and sometimes also actuators, often have to be replaced by similar, yet not identical, components. At a higher level, the target vehicle often changes during the work process. The presented software provides a framework that supports these replacements and allows configurations of sensors, actuators, and target machines to be specified and manipulated in an efficient manner. The system can be distributed over a network of computers if some software modules require more computing power, i.e. more hardware can be added to the system without any software changes. To accomplish sufficient monitoring of the system's health, a dedicated system keeps track of all software modules. The system uses logfiles to enable convenient debugging and performance analysis of hardware and software modules. The software has been developed as part of, and is currently in use in, a R&D-project for an autonomous path-tracking forest machine.
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| 2. |
- Billing, Erik, 1981-, et al.
(författare)
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A formalism for learning from demonstration
- 2010
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Ingår i: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 1:1, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as mappings between some of these spaces. Finally, behavior primitives are introduced as one example of good bias in learning, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination. The formalism is exemplified through a sequence learning task where a robot equipped with a gripper arm is to move objects to specific areas. The introduced concepts are illustrated with special focus on how bias of various kinds can be used to enable learning from a single demonstration, and how ambiguities in demonstrations can be identified and handled.
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| 3. |
- Billing, Erik, 1981-, et al.
(författare)
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Behavior recognition for learning from demonstration
- 2010
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Ingår i: 2010 IEEE International Conference on Robotics and Automation. - : IEEE. - 9781424450404 - 9781424450381 ; , s. 866-872
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Konferensbidrag (refereegranskat)abstract
- Two methods for behavior recognition are presented and evaluated. Both methods are based on the dynamic temporal difference algorithm Predictive Sequence Learning (PSL) which has previously been proposed as a learning algorithm for robot control. One strength of the proposed recognition methods is that the model PSL builds to recognize behaviors is identical to that used for control, implying that the controller (inverse model) and the recognition algorithm (forward model) can be implemented as two aspects of the same model. The two proposed methods, PSLE-Comparison and PSLH-Comparison, are evaluated in a Learning from Demonstration setting, where each algorithm should recognize a known skill in a demonstration performed via teleoperation. PSLH-Comparison produced the smallest recognition error. The results indicate that PSLH-Comparison could be a suitable algorithm for integration in a hierarchical control system consistent with recent models of human perception and motor control.
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| 4. |
- Billing, Erik, 1981-, et al.
(författare)
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Behavior recognition for segmentation of demonstrated tasks
- 2008
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Ingår i: IEEE SMC International Conference on Distributed Human-Machine Systems (DHMS). - 9788001040270
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Konferensbidrag (refereegranskat)abstract
- One common approach to the robot learning technique Learning From Demonstration, is to use a set of pre-programmed skills as building blocks for more complex tasks. One important part of this approach is recognition of these skills in a demonstration comprising a stream of sensor and actuator data. In this paper, three novel techniques for behavior recognition are presented and compared. The first technique is function-oriented and compares actions for similar inputs. The second technique is based on auto-associative neural networks and compares reconstruction errors in sensory-motor space. The third technique is based on S-Learning and compares sequences of patterns in sensory-motor space. All three techniques compute an activity level which can be seen as an alternative to a pure classification approach. Performed tests show how the former approach allows a more informative interpretation of a demonstration, by not determining "correct" behaviors but rather a number of alternative interpretations.
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| 5. |
- Billing, Erik, 1981-, et al.
(författare)
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Formalising learning from demonstration
- 2008
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. Inspired by the work on planning and actuation by LaValle, common LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as the mappings between some of these spaces. Finally, behavior primitives are introduced as one example of useful bias in the learning process, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination.
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| 6. |
- Billing, Erik, 1981-, et al.
(författare)
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Model-free learning from demonstration
- 2010
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Ingår i: ICAART 2010 - Proceedings of the international conference on agents and artificial intelligence. - Portugal : INSTICC. - 9789896740221 ; , s. 62-71
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Konferensbidrag (refereegranskat)abstract
- A novel robot learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated. PSL is a model-free prediction algorithm inspired by the dynamic temporal difference algorithm S-Learning. While S-Learning has previously been applied as a reinforcement learning algorithm for robots, PSL is here applied to a Learning from Demonstration problem. The proposed algorithm is evaluated on four tasks using a Khepera II robot. PSL builds a model from demonstrated data which is used to repeat the demonstrated behavior. After training, PSL can control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. PSL was able to successfully learn and repeat the first three (elementary) tasks, but it was unable to successfully repeat the fourth (composed) behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.
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| 7. |
- Billing, Erik, 1981-, et al.
(författare)
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Predictive learning from demonstration
- 2011. - 1
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Ingår i: Agents and artificial Intelligence. - Berlin : Springer Verlag. - 9783642198892 - 9783642198908 ; , s. 186-200
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Bokkapitel (refereegranskat)abstract
- A model-free learning algorithm called Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL is inspired by several functional models of the brain. It constructs sequences of predictable sensory-motor patterns, without relying on predefined higher-level concepts. The algorithm is demonstrated on a Khepera II robot in four different tasks. During training, PSL generates a hypothesis library from demonstrated data. The library is then used to control the robot by continually predicting the next action, based on the sequence of passed sensor and motor events. In this way, the robot reproduces the demonstrated behavior. PSL is able to successfully learn and repeat three elementary tasks, but is unable to repeat a fourth, composed behavior. The results indicate that PSL is suitable for learning problems up to a certain complexity, while higher level coordination is required for learning more complex behaviors.
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| 8. |
- Billing, Erik, 1981-, et al.
(författare)
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Predictive Learning in Context
- 2010
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Ingår i: Proceedings of the tenth international conference on epigenetic robotics. - Lund, Sweden : Lund University. - 9789197738095 ; , s. 157-158
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Konferensbidrag (refereegranskat)
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| 9. |
- Billing, Erik, 1981-, et al.
(författare)
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Robot learning from demonstration using predictive sequence learning
- 2012
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Ingår i: Robotic systems. - Kanpur, India : IN-TECH. - 9789533079417 ; , s. 235-250
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Bokkapitel (refereegranskat)abstract
- In this chapter, the prediction algorithm Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL generates hypotheses from a sequence of sensory-motor events. Generated hypotheses can be used as a semi-reactive controller for robots. PSL has previously been used as a method for LFD, but suffered from combinatorial explosion when applied to data with many dimensions, such as high dimensional sensor and motor data. A new version of PSL, referred to as Fuzzy Predictive Sequence Learning (FPSL), is presented and evaluated in this chapter. FPSL is implemented as a Fuzzy Logic rule base and works on a continuous state space, in contrast to the discrete state space used in the original design of PSL. The evaluation of FPSL shows a significant performance improvement in comparison to the discrete version of the algorithm. Applied to an LFD task in a simulated apartment environment, the robot is able to learn to navigate to a specific location, starting from an unknown position in the apartment.
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| 10. |
- Billing, Erik, 1981-, et al.
(författare)
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Robot learning from demonstration using predictive sequence learning
- 2011
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Ingår i: Robotic systems. - Kanpur, India : IN-TECH. - 9789533079417 ; , s. 235-250
-
Bokkapitel (refereegranskat)abstract
- In this chapter, the prediction algorithm Predictive Sequence Learning (PSL) is presented and evaluated in a robot Learning from Demonstration (LFD) setting. PSL generates hypotheses from a sequence of sensory-motor events. Generated hypotheses can be used as a semi-reactive controller for robots. PSL has previously been used as a method for LFD, but suffered from combinatorial explosion when applied to data with many dimensions, such as high dimensional sensor and motor data. A new version of PSL, referred to as Fuzzy Predictive Sequence Learning (FPSL), is presented and evaluated in this chapter. FPSL is implemented as a Fuzzy Logic rule base and works on a continuous state space, in contrast to the discrete state space used in the original design of PSL. The evaluation of FPSL shows a significant performance improvement in comparison to the discrete version of the algorithm. Applied to an LFD task in a simulated apartment environment, the robot is able to learn to navigate to a specific location, starting from an unknown position in the apartment.
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