Grounded Representation Driven Robot Motion ... - Semantic Scholar

Grounded Representation Driven Robot Motion Design Michael Trieu and Mary-Anne Williams Innovation and Technology Research Laboratory University of Technology, Sydney, NSW 2007 Australia [email protected], [email protected]

Abstract. Grounding robot representations is an important problem in Artificial Intelligence. In this paper we show how a new grounding framework guided the development of an improved locomotion engine [3] for the AIBO. The improvements stemmed from higher quality representations that were grounded better than those in the previous system [1]. Since the AIBO is more grounded under the new locomotion engine it makes better decisions and achieves its design goals more efficiently. Furthermore, a well grounded robot offers significant software engineering benefits since its behaviours can be developed, debugged and tested more effectively. Keywords: Robotics, Perception, Knowledge Representation.

1 Introduction In order for a robot to achieve its objectives it must ground its representations: a grounded representation is one where the entities in the representation correspond meaningfully to the entities they represent [2, 7, 10]. In this paper we use a new grounding framework [12] to drive the design of a robot locomotion system and describe the value and benefits derived from that design. The main idea is that the grounding framework can not only be used at a theoretical level to analyse, evaluate and compare grounding capabilities in robots, but it also offers a practical guide to assist the design and construction of more reliable and adaptable robots. A major aim of modern science and engineering is to deploy dependable and flexible systems that are easy and cost effective to manage over their lifetime as their requirements and surrounding environment evolves and changes. Achieving this aim for systems like robots operating in complex and dynamic environments has proved to be extremely challenging. A poor understanding of grounding has been identified as major research bottleneck [2, 6, 7, 9, 10, 11]. In essence, the grounding problem is the challenge of designing and managing internal representations so that they meaningfully reflect the entities they are supposed to be representing. For example, how do we design representations of a robot’s body so that it that can be appropriately managed by an intelligent control and behaviour system. Grounding involves building and maintaining coherent representations that correspond meaningfully to the entities they represent, whether the entities are physical, abstract, sensed, perceived, postulated, or simulated. A major challenge in addressing the grounding problem is not only in designing high quality representations as such but designing representations that are adaptable and conducive to change. In section 2 we describe the U. Visser et al. (Eds.): RoboCup 2007, LNAI 5001, pp. 520–527, 2008. © Springer-Verlag Berlin Heidelberg 2008

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grounding problem and the new grounding framework [12]. An AIBO robot system [1, 3] is described in section 3, in particular AIBO body, sensors and actuators, and the new Locomotion Engine. Section 4 describes the grounded representation guided design, and demonstrates how a grounding approach can drive design and development towards more resilient and reliable robotic systems.

2 The Grounding Problem According to Brooks [3] “the world is its own best representation”, and so if a robot only had to deal with the current state of the world then there is no necessity for representations. Since future world states in general are not a feature of the current world state, robots that need to plan and anticipate future world states in order to achieve their design goals require representations. Furthermore, the better a robot’s representations are grounded, the more effectively it will achieve its goals, the more appropriate its behaviours and the higher the quality of its decisions. As a result robot representation design is a key area of interest in Artificial Intelligence. A grounded representation does not require that every entity in the representation be linked to a corresponding physical manifestation, but that a meaningful relationship exists between the entities in the representation and the entities being represented [12]. Maintaining a correspondence between representations of physical objects and the objects themselves is important but so too are the representations of object functionalities, relationships between objects, and as well as the descriptions of ways to interact with specific objects, etc. For the purpose of understanding grounding in robots, it is insightful to classify representations using the hierarchy of Gärdenfors [5] which describes the crucial relationships between three key representational entities: sensations, perceptions, and simulations. Sensations are immediate sensorimotor impressions, perceptions are interpreted/processed sensorimotor impressions, and simulations are detached representations, i.e. they are not tied to perceptions of the current state of the world. Sensations provide systems with an awareness of the external world and their internal world. They exist in the present, are localised in the body/system, and are modality specific, e.g. visual, auditory, not both. Perceptions encapsulate more information than raw sensorimotor information [2, 5]. Representations can be derived from information that has been gathered from a wide range of sources e.g. internal and external sensors, internal and external effectors, external instruments, external systems, etc. In this paper we focus on grounding representations derived from cued internal sensations and perceptions generated from a robot’s body. The grounding framework [12] is motivated by the need to understand and build sophisticated systems such as robots that do (some of) the grounding themselves rather than systems that are completely grounded with the assistance of human grounding capabilities. It comprises five essential elements which can be as detailed as required for the purpose of the analysis: 1. 2. 3. 4. 5.

System Objectives – description of the system objective, goals, tasks & activities Architecture of Grounding Capability - a description of the underlying system architecture that supports or implements the grounding capability. Scope of the Analysis - a detailed description of the scope of the analysis. Nature of the Grounding Capability – relative to the grounding architecture. Groundedness Qualities - includes a description of the pertinent groundedness qualities relative to each architectural component of the grounding capability.

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All five components of the framework are related, e.g. the objectives and the scope will often determine how the qualities of groundedness are selected and assessed. The groundedness qualities identified as crucial to improving the Locomotion Engine are: faithfulness, correctness, transparency, accuracy, self-awareness, flexibility, adaptability and robustness.

3 AIBO Robot System and Locomotion Engine The AIBO is a four legged robot developed by Sony. The main AIBO sensors and actuators are illustrated in Figure 1 below. Internal motors are used to move the AIBO body parts. The mouth has one degree of freedom; each leg has three, each ear one, and the tail two degrees of freedom. Stereo Microphone Tactile Head Touch Sensor

Wireless Status LED

Illume-Face LED Panel

IEEE 802.11b Wireless

Distance Sensor

Mode LED

Chin Sensor

Pause Button

Back LED Tactile Back Sensors

576MHz 64bit RISC CPU

Back Features

Acceleration Sensor Vibration Sensor

360K-pixel Image Sensor Edge Detection Sensor

Wireless Switch Volume Switch

Speaker, MIDI Speaker

Paw Sensors

Front Features

Fig. 1. AIBO sensors and actuators [Source: www.sony.com]

Our robotic system architecture [1, 4] is designed for soccer play on an AIBO platform. In the remainder of the paper we focus on the grounding of a robot’s representations for improving the design of locomotion and behaviour. The Locomotion Engine [4] calculates and controls all locomotive movement of the robots. The robots walk with the bent fore-elbow stance adopted by most teams in the RoboCup Four-leggedleague. The engine uses a static gait for all walking motions on the field. This involves the synchronous movement of diagonally paired legs; that is, the front left paw moves in synchronization with the back right paw while the other pair moves out of phase by half a step, as illustrated in Figure 2. Paw location during movement air

ground

= Interruptible Position Front Right Front Left Uninterruptible Half Step

Back Right

Uninterruptible Half Step

Back Left

Time

Fig. 2. Static gait

Extreme value

Home Position

Extreme value

Fig. 3. Rectangular locus in detail


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Each paw follows a particular locus and is interruptible at two specific points only as shown in Figure 3. A full step is when the paw moves around the locus and returns to its initial position and a half step is when the paw moves from one interruptible position to the other. This simple yet effective gait allows for desirable speed and stability as there is always two legs in contact with the ground at any given time. It is sometimes required that the robots move to a stable position before performing an action such as a kick. This stable position occurs when all four paws are situated at the home positions of their corresponding loci. The home position is the interruptible position located on the ground, which lies between the two extreme locus values as shown in Figure 3. The path each foot takes when in the air depends on the particular locus used. The locus path guides the robots paw through the air in a particular pattern and along the ground. Several successfully implemented loci including rectangle, ellipse, and raised rectangle each give rise to different walk types in terms of speed and stability. During the process of a step, the engine calculates the next point to move the robots paw on the chosen locus. It then uses this point to calculate the individual actuator joints by means of inverse kinematics and the parameters of the given walk. Controlling the walk stance, direction and speed requires five input parameters. These inputs are determined by an external module that commands the robot to complete an action. The input parameters are: type of walk/stance, forwards movement, strafe (sideways movement), turning movement, and speed. These input parameters are passed to the behaviour control engine when the robot decides to move. Each command is delivered as input parameters, which the engine uses to calculate the next uninterruptible half step to perform. The half steps are uninterruptible in that once they commence, they must complete regardless of any new commands. This allows the robot to take on a stable stance before commencing further action such as another half step or a kick. Each of these calculated steps incorporates a combinational movement of the Forward, Strafe and Turn directions. Each walk type corresponds to a particular set of unique parameters. They are fine-tuned for use in different situations by adjusting the many parameters associated with each leg. Having parameters associated with each leg allows total independent leg control and unique locomotive actions. The parameters for each of the four legs are shown in Figure 6: Bounce Height – the amount of bounce of each leg modeled over a sinusoid; Shoulder Height – the distance between the shoulder and the ground; Step Height – the maximum height at which the paw is lifted off the ground; Step Position X – the side distance between the paws home position & body; Step Position Y – the forward distance between paws home position & body.

4 Grounded Representation Driven Design The use of a grounded representation design in the AIBO Locomotion Engine [1] led to major improvements to the robot soccer team [4]. The groundedness qualities we identified as being crucial to develop in the new design are (listed in order of priority): faithfulness, correctness, transparency, accuracy, self-awareness, flexibility, adaptability and robustness. Several new features were developed as a result of our grounded representation driven approach such as the following major enhancements to previous designs: compensation for asymmetric weight distribution, interpolated actions, action interrupt abilities, independent loci and variable locus points, independent binary file, and Delta factor for the ERS-7 model of the AIBO. Compensation for asymmetric weight distribution: The robot movements are slightly biased to one side as a due to the uneven weight distribution of the robot which results from the off-centre placement of the robot’s lithium battery. This has a significant impact

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on the walking direction of the robot if all four legs perform the same walking motions without calibration. An advantage of having independent representations for legs means that calibration of straight-line movement can be achieved by use of factors that allow each leg to have weighted movements, that is, particular legs are permitted to move more then others in a single step. The movements are weighted in units of percentage. There is a factor for every direction and leg combination, which gives rise to a large number of factor parameters (six factors × four legs), however this representation gives the AIBO the potential of achieving maximal control of its limbs. Each walk type has its own set of factors which are applied to each leg: Forward Factor, Backward Factor, Turn Right Factor, Turn Left Factor, Strafe Right Factor, and Strafe Left Factor. Without calibration, experiments show that the robot moves off to the right when trying to walk straight ahead. By adjusting the front Forward Factors so that the front left paw moves less than the front right paw, a slight pivot point is placed upon the front left paw. The pivot forces the robot to correct the biased movement, which results in straight-line movement as initially expected.

Straight-Line Movement

Front-Left Forward Factor

Back-Left Forward Factor

Not Calibrated

Pivot Point

Front-Right Forward Factor

Front-Left Forward Factor

Back-Right Forward Factor

Back-Left Forward Factor

Straight-Line Movement

Front-Right Forward Factor

Calibrated

Back-Right Forward Factor

Fig. 4. Calibration using Independent Factors

These factors allow the AIBO to perform unique walks and kicks and other movements, which can be used for specific situations such as quick ball-handling maneuvers. The grounded control that the factors provide gives an unlimited freedom of configurable motions for game play, and ensures that the robots representations of motion are the well grounded in accordance with design criteria. Interpolated Actions: All robot actions which include kicks, special movements and get up routines are made up of a number of position frames. Each position frame consists of a string of positions for every actuator on the robot. Actions on our previous design [1] were based on the hold count parameter on every frame. Sequencing through an action meant that the robot would snap to each position frame and hold it there according to the hold count in seconds. The new more grounded Locomotion Engine incorporates an extra parameter called the interpolation count. This parameter determines how fast (in seconds) each position frame is interpolated as demonstrated in the example below where knee angles are given in degrees. In the previous design FrontLeftKnee [1] had its hold count set to 0.8 seconds, and in the new design [4] it has


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its interpolation count at 0.5 seconds and hold count set to 0.3 seconds. Interpolated actions allow for smoother robot motion and hence improve the robots stability and grounding. Not only does this prevent slipping but also improves vision quality when the robot is in motion. From a grounding perspective the interpolation version is more faithful to the motion of the robot than the un-interpolated version. Furthermore, not only did this allow smooth actions but also a less dependency on the behaviour module to move to points between two positions. This provided the locomotion engine more control over the robots movements and hence increased the AIBO’s self-awareness. Action Interruptibilities: The new more grounded Locomotion Engine allows each action to fall in one of the following four categories: (i) Not Interruptible, (ii) Head Interruptible, (iii) Legs Interruptible, and (iv) Action Interruptible. The interruptibility of an action allows actions and walks the ability to override other actions when necessary. This allows complex sequences of actions to be performed during runtime. Not Interruptible means that the action cannot be interrupted by anything once it is started with the exception of the get up routine. Head interruptible allows only the head to be controlled by another action, even if the overriding action contains other movements besides the head, only the head movements will be executed with everything else continuing with the previous action. Legs Interruptible allows only the legs to be controlled by another action, e.g. if the robot wished to complete a head kick and walk forward at the same time, the head kick must be set to Legs Interruptible. Action Interruptible allows the entire robot to be overridden by another action or walk. Action interpolation and interruption capabilities together endow the AIBO with an important highly grounded stability awareness. In our previous version; actions, kicks and walks were managed by a behavior module that included knowledge of when to change from one action to another. If the behaviour module was not well grounded then it did not do this correctly or in an orderly fashion, and as a result the robot often became unstable. In the new grounded version actions, kicks, and walks each had new interruptibility parameters which prevented an action to occur during another at crucial moments. This ensures that the robot remains stable. Independent Loci and Variable Locus Points: The previous locomotion system was limited in that there were only three loci shapes to choose for the walk engine as explained earlier, and all legs were required to use the same locus. In our new Locomotion Engine the front and back legs have independent loci with each locus containing a number of user defined points. This flexibility ensures that the Locomotion Engine is able to find a fast walk using reinforcement learning algorithms. Each point of the locus has three parameters, X, Y and Time. X and Y determine the location of the point and Time determines the percentage of time between specific points. The number of locus points can also be chosen to create many different shapes, thereby increasing the flexibility of the AIBO’s movements. Delta Factors: The new grounded Locomotion Engine is implemented on the ERS-7 and its predecessor AIBO 210. The ERS-7’s body is in a polished plastic casing and the leg surface it much smoother than the AIBO 210s, which lead to a number of problems when ERS-7s were turning. In order to keep the ERS-7 robots grounded an additional factor was introduced into the walking parameters, namely the Delta factor. The Delta factor essentially determines how much skid steering the robot should complete depending on the amount of turn required as illustrated in Figure 5. By using skid steering, the robot is essentially moving like a tank when turning. To turn

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Without Delta Factor [ERS-7]

With Delta Factor [ERS-7]

Fig. 5. No Delta Factor and Delta Factor – Turning Right Example

right as shown in Figure 8, the legs on the right must move as if the robot is moving backwards whereas the legs on the left must move as if the robot was going forwards. The Delta factor determines how much of this behaviour occurs whilst turning. The introduction of the Delta factor lead to a heightened awareness of walk calibration so that when the AIBO’s control module instructed it to move forward then it did so in a straight line. In our new grounded locomotion engine each limb has calibration factors that are customized to each different walk. The introduction of factors removed the previous need for the behaviour module to compensate for miscalibrations and allowed it to specify motion without having to take weight distribution into account since the new more grounded Localization Engine that directly since it had a heightened awareness of the relationship between body movements and higher level goals in terms of movement. The Delta factor determines how much of this behaviour occurs whilst turning. Reconfigurable Walks, Actions and Kicks: The new grounded Locomotion Engine is capable of adding and updating all walks, actions and kicks on-the-fly. The configuration is completed via text files which can be updated via wireless communication while the robot is in play. In this way the robots movements are detached from specific client applications. This flexible configuration allows any application that can alter text files the ability to configure and create any new walks, actions or kicks. In terms of groundedness this additional feature not only increases the AIBO’s flexibility but also its transparency therefore enhancing its grounding capability. Locomotion Trainer: In order to meet the grounding requirements for transparency we built the so called Locomotion Trainer V2 which is a Microsoft Windows based tool that is used to calibrate and remotely control the robots. It allows the creation of actions and walks. All parameters can be changed on the fly and the results can be observed immediately. Successful sets of parameters can be saved and stored into *.txt files for use on the robots. The Trainer can also monitor the odometry readings. Calibration of walk factors can be completed using this program. Learning to Walk: A major advantage of the new grounded Locomotion Engine is that is offers significantly more scope for learning than the previous version since the


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locomotion parameters are more grounded within the AIBO’s body and also more transparently integrated into the robots control system. The new grounded design supports a wide range of algorithms including genetic algorithms, reinforcement learning, and human assisted learning. Unsurprisingly, we found that a combination of approaches led to the best results. Speed was not the only important factor in determining the machine learning techniques. Other factors included smoothness so that the robot’s camera did not bounce around causing difficulties with vision and stability so that the robot would not fall over, particularly when coming to a halt.

5 Conclusion In this paper we have advocated a grounded representation driven approach to robot design based on a new grounding framework [12], and we illustrated the following benefits using an AIBO Locomotion Engine: (i) heightened awareness of stability and calibration, (ii) improved ability to respond to falling over, (iii) improved ability to turn with skid steering, and (iv) improved ability to learn new actions. The grounding driven design exhibits a range of desirable properties such as faithfulness, correctness, transparency, accuracy, self-awareness, flexibility, adaptability and robustness. The AIBO representations were more faithful to the robot body state, i.e. the actual body part locations against measured locations of actuators; they are also more correct and accurate. The design offered new flexibilities and as a result supported a high degree of adaptability in locomotion and higher level behaviours. The AIBO attained a higher level of awareness in the new design and due to the grounded motion, and behaviour was more robust and resilient.

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