Postural modes and control for dexterous mobile manipulation: the UMass uBot concept

Ruiken, Dirk; Lanighan, Michael W.; Grupen, Roderic A.

doi:10.1109/humanoids.2013.7029988

Cited by 19 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, optimal controllers described by regulators like linear quadratic regulators (LQRs) and its variants fall into this categorization as well. In fact, dynamically balancing robots like the uBot platforms Kuindersma et al (2009); Ruiken et al (2013), implement a variant of LQR. To reiterate the notion of learning abstractions, we suggest strongly that rather than learning a balancing policy from scratch, perhaps a better direction is to consider this closed-loop controller as a skill and employing learning architectures at the skill level of abstraction.…”

Section: Activating Motion Primitivesmentioning

confidence: 99%

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Wong¹

2016

Preprint

View full text Add to dashboard Cite

Despite outstanding success in vision amongst other domains, many of the recent deep learning approaches have evident drawbacks for robots. This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts. These recent advances are only a piece to the puzzle. We suggest that deep learning as a tool alone is insufficient in building a unified framework to acquire general intelligence. For this reason, we complement our survey with insights from cognitive development and refer to ideas from classical control theory, producing an integrated direction for a lifelong learning architecture.

show abstract

Section: Activating Motion Primitivesmentioning

confidence: 99%

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Wong¹

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…Experiments are done on a dynamic simulation of the uBot-6 platform, a 13 DOF, toddler-sized, dynamically balancing, mobile manipulator [34] equipped with an Asus Xtion Pro Live RGB-D camera (shown in Figure 2) and two ATI Mini45 Force/Torque sensors one in each hand (not shown in figure). Control actions are executed by the robot to establish new sensor geometries and reveal new aspects.…”

Section: A Robot Platformmentioning

confidence: 99%

Intrinsically motivated multimodal structure learning

Wong

Grupen

2016

2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

Self Cite

View full text Add to dashboard Cite

Abstract-We present a long-term intrinsically motivated structure learning method for modeling transition dynamics during controlled interactions between a robot and semipermanent structures in the world. In particular, we discuss how partially-observable state is represented using distributions over a Markovian state and build models of objects that predict how state distributions change in response to interactions with such objects. These structures serve as the basis for a number of possible future tasks defined as Markov Decision Processes (MDPs). The approach is an example of a structure learning technique applied to a multimodal affordance representation that yields a population of forward models for use in planning. We evaluate the approach using experiments on a bimanual mobile manipulator (uBot-6) that show the performance of model acquisition as the number of transition actions increases.

show abstract

“…uBot-6 can transition between its postural modes by following the paths in the postural transition graph (Figure 3). The postural modes, controllers, and costs of postural transitions are described in detail in (Kuindersma et al 2009) and (Ruiken, Lanighan, and Grupen 2013). Compared to locomotion, transitions from one mode to another are relatively costly-in fact, they are an order of magnitude more expensive in terms of time and energy.…”

Section: Robot Platformmentioning

confidence: 99%

“…Table 1 shows the required time for driving in various postural modes and transitions between postural modes. Experiments use nominal velocities for driving actions though more detailed cost models and velocity constraints can be found in (Ruiken, Lanighan, and Grupen 2013). Prone scooting allows higher maximum drive velocities than balancing.…”

Section: Cost Modelingmentioning

confidence: 99%

Path Planning for Dexterous Mobility

Ruiken

Lanighan

Grupen

2014

ICAPS

Self Cite

View full text Add to dashboard Cite

In order to overcome a large variety of run-time constraints, robots are being designed to be more resourceful by incorporating more sensory and motor options for any given task. The added flexibility provides a basis for dexterous problem solving, but challenges planners by increasing the complexity of search. Moreover, the cost of functionally equivalent options can vary dramatically. In the worst case, naive approaches to planning avoid expensive actions until inexpensive options are explored exhaustively leading to poor overall search performance. We present a dexterous robot that introduces multiple types of locomotor actions with significant differences in cost and situational value and apply standard search techniques to demonstrate the additional challenges that arise in the context of dexterous mobility. Results highlight incentives, opportunities, and impact for overcoming these challenges. Additionally, we present a prototype for a path planner that uses environmental features to define an efficient set of subgoals for dexterous motion planning.

show abstract

Postural modes and control for dexterous mobile manipulation: the UMass uBot concept

Cited by 19 publications

References 13 publications

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Intrinsically motivated multimodal structure learning

Path Planning for Dexterous Mobility

Contact Info

Product

Resources

About