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Philip Long

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12 papers
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12

ICRA Conference 2024 Conference Paper

CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation

  • Aayush Jain
  • Philip Long
  • Valeria Villani
  • John D. Kelleher
  • Maria Chiara Leva

Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees. CoBT relies on a single demonstration and a combination of data-driven machine learning methods with logic-based declarative learning to learn a task, thus eliminating the need for programming expertise or long development times. The proposed framework is experimentally validated on 7 manipulation tasks and we show that CoBT achieves ≈ 93% success rate overall with an average of 7. 5s programming time. We conduct a pilot study with non-expert users to provide feedback regarding the usability of CoBT. More videos and generated behavior trees are available at: https://github.com/jainaayush2006/CoBT.git.

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ICRA Conference 2020 Conference Paper

Tuning-Free Contact-Implicit Trajectory Optimization

  • Aykut Özgün Önol
  • Radu Corcodel
  • Philip Long
  • Taskin Padir

We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.

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ICRA Conference 2019 Conference Paper

Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification

  • Aykut Özgün Önol
  • Philip Long
  • Taskin Padir

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.

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IROS Conference 2019 Conference Paper

Optimization based Trajectory Planning of Mobile Cable-Driven Parallel Robots

  • Tahir Rasheed
  • Philip Long
  • Adolfo Suarez-Roos
  • Stéphane Caro

A Mobile Cable-Driven Parallel Robot (MCDPR) is composed of a classical Cable-Driven Parallel Robot (CDPR) carried by multiple mobile bases. The additional mobilities due the motion of the mobile bases allow such systems to autonomously modify their geometric architecture, and thus make them suitable for multiple manipulation tasks in constrained environments. Moreover, these additional mobilities mean MCDPRs are kinematically redundant and may use this redundancy to optimize secondary task criteria. However, the high dimensional state space and closed chain constraints add complexity to the motion planning problem. To overcome this, we propose a method for trajectory planning for MCDPRs performing pick and place operations in cluttered environments by using direct transcription optimization. Two different scenarios have been considered and their results are validated using a dynamic simulation software (V-REP) and experimentally.

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ICRA Conference 2019 Conference Paper

optimization-Based Human-in-the-Loop Manipulation Using Joint Space Polytopes

  • Philip Long
  • Tarik Kelestemur
  • Aykut Özgün Önol
  • Taskin Padir

This paper presents a new method of maximizing the free space for a robot operating in a constrained environment under operator supervision. The objective is to make the resulting trajectories more robust to operator commands and/or changes in the environment. To represent the volume of free space, the constrained manipulability polytopes are used. These polytopes embed the distance to obstacles, the distance to joint limits and the distance to singular configurations. The volume of the resulting Cartesian polyhedron is used in an optimization-based motion planner to create the trajectories. Additionally, we show how fast collision-free inverse kinematic solutions can be obtained by exploiting the pre-computed inequality constraints. The proposed algorithm is validated in simulation and experimentally.

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IROS Conference 2018 Conference Paper

A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation

  • Aykut Özgün Önol
  • Philip Long
  • Taskin Padir

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1)a contact model that is based on complementarity constraints, (2)a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.

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ICRA Conference 2018 Conference Paper

Available Wrench Set for Planar Mobile Cable-Driven Parallel Robots

  • Tahir Rasheed
  • Philip Long
  • David A. Marquez-Gamez
  • Stéphane Caro

Cable-Driven Parallel Robots (CDPRs) have several advantages over conventional parallel manipulators most notably a large workspace. CDPRs whose workspace can be further increased by modification of the geometric architecture are known as Reconfigurable Cable Driven Parallel Robots(RCDPRs). A novel concept of RCDPRs, known as Mobile CDPR (MCDPR) that consists of a CDPR carried by multiple mobile bases, is studied in this paper. The system is capable of autonomously navigating to a desired location then deploying to a standard CDPR. In this paper, we analyze the Static equilibrium (SE) of the mobile bases when the system is fully deployed. In contrast to classical CDPRs we show that the workspace of the MCDPR depends, not only on the tension limits, but on the SE constraints as well. We demonstrate how to construct the Available Wrench Set (AWS) for a planar MCDPR wih a point-mass end-effector using both the convex hull and Hyperplane shifting methods. The obtained results are validated in simulation and on an experimental platform consisting of two mobile bases and a CDPR with four cables.

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IROS Conference 2014 Conference Paper

Force/vision control for robotic cutting of soft materials

  • Philip Long
  • Wisama Khalil
  • Philippe Martinet

In this paper, a force/vision control strategy is proposed in order to separate soft deformable materials using cooperative robots. The separation is performed by repeating a series of cuts, called passages, along a curved trajectory. The vision control is used to locally update the robot trajectory in response to both on-line deformations and off-line modeling errors. The force controller is used to ensure that the cut is performed without global deformation or damage to the surrounding area. The second robot is used to facilitate the cutting by applying external forces to the object. The control scheme is validated experimentally by cutting soft foam material.

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NeurIPS Conference 2006 Conference Paper

Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions

  • Philip Long
  • Rocco Servedio

We consider the well-studied problem of learning decision lists using few examples when many irrelevant features are present. We show that smooth boosting algorithms such as MadaBoost can efficiently learn decision lists of length k over n boolean variables using poly(k, log n) many examples provided that the marginal distribution over the relevant variables is "not too concentrated" in an L 2 -norm sense. Using a recent result of Hastad, we extend the analysis to obtain a similar (though quantitatively weaker) result for learning arbitrary linear threshold functions with k nonzero coefficients. Experimental results indicate that the use of a smooth boosting algorithm, which plays a crucial role in our analysis, has an impact on the actual performance of the algorithm.

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NeurIPS Conference 2004 Conference Paper

Mistake Bounds for Maximum Entropy Discrimination

  • Philip Long
  • Xinyu Wu

We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a general bound proved for the Weighted Majority Algorithm, and similar to bounds for other variants of Winnow. We prove a more refined bound that leads to a nearly opti- mal algorithm for learning disjunctions, again, based on the maximum entropy principle. We describe a simplification of the on-line maximum entropy method in which, after each iteration, the margin constraints are replaced with a single linear inequality. The simplified algorithm, which takes a similar form to Winnow, achieves the same mistake bounds.

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NeurIPS Conference 1999 Conference Paper

The Relaxed Online Maximum Margin Algorithm

  • Yi Li
  • Philip Long

We describe a new incremental algorithm for training linear thresh(cid: 173) old functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen ex(cid: 173) amples correctly with the maximum margin. It is known that such a maximum-margin hypothesis can be computed by minimizing the length of the weight vector subject to a number of linear constraints. ROMMA works by maintaining a relatively simple relaxation of these constraints that can be efficiently updated. We prove a mistake bound for ROMMA that is the same as that proved for the perceptron algorithm. Our analysis implies that the more computationally intensive maximum-margin algo(cid: 173) rithm also satisfies this mistake bound; this is the first worst-case perfor(cid: 173) mance guarantee for this algorithm. We describe some experiments us(cid: 173) ing ROMMA and a variant that updates its hypothesis more aggressively as batch algorithms to recognize handwritten digits. The computational complexity and simplicity of these algorithms is similar to that of per(cid: 173) ceptron algorithm, but their generalization is much better. We describe a sense in which the performance of ROMMA converges to that of SVM in the limit if bias isn't considered.

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