Publications

TRI Author: Vitor Guizilini

All Authors: Vitor Guizilini, Ransalu Senanayake, Fabio Ramos

This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. This data-efficient model not only allows us to learn the occupancy states incrementally, but also makes predictions about what the future occupancy states will be. Experiments performed using 2D and 3D laser data collected from crowded unstructured outdoor environments show that the proposed methodology can accurately predict occupancy states for areas of around 1000 m 2 at 10 Hz, making the proposed framework ideal for online applications under real-time constraints. Read More

Citation: Guizilini, Vitor, Ransalu Senanayake, and Fabio Ramos. "Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments." In 2019 International Conference on Robotics and Automation (ICRA), pp. 4091-4097. IEEE, 2019.

TRI Authors: Hongkai Dai, Russ Tedrake

All Authors: Hongkai Dai, Gregory Izatt, Russ Tedrake

In this paper, we present a novel formulation of the inverse kinematics (IK) problem with generic constraints as a mixed-integer convex optimization program. The proposed approach can solve the IK problem globally with generic task space constraints: a major improvement over existing approaches, which either solve the problem in only a local neighborhood of the user initial guess through nonlinear non-convex optimization, or address only a limited set of kinematics constraints. Specifically, we propose a mixed-integer convex relaxation of non-convex SO(3) rotation constraints, and apply this relaxation on the IK problem. Our formulation can detect if an instance of the IK problem is globally infeasible, or produce an approximate solution when it is feasible. We show results on a seven-joint arm grasping objects in a cluttered environment, an 18-degree-of-freedom quadruped standing on stepping stones, and a parallel Stewart platform. Moreover, we show that our approach can find a collision free path for a gripper in a cluttered environment, or certify such a path does not exist. We also compare our approach against the analytical approach for a six-joint manipulator. The open-source code is available at http://drake.mit.edu.  Read More

Citation: Dai, Hongkai, Gregory Izatt, and Russ Tedrake. "Global inverse kinematics via mixed-integer convex optimization." The International Journal of Robotics Research 38, no. 12-13 (2019): 1420-1441.

TRI Author: Simon Stent

All Authors: Robert Semmens, Nikolas Martelaro, Pushyami Kaveti, Simon Stent, Wendy Ju

Advances in automotive sensing systems and speech interfaces provide new opportunities for smarter driving assistants or infotainment systems. For both safety and consumer satisfaction reasons, any new system which interacts with drivers must do so at appropriate times. We asked 63 drivers, ''Is now a good time?'' to receive non-driving information during a 50-minute drive. We analyzed 2,734 responses and synchronized automotive and video data, and show that while the chances of choosing a good time can be determined with better success using easily accessible automotive data, certain nuances in the problem require a richer understanding of the driver and environment states in order to achieve higher performance. We illustrate several of these nuances with quantitative and qualitative analyses to contribute to the understanding of how to design a system that might simultaneously minimize the risk of interacting at a bad time while maximizing the window of allowable interruption. Read More

Citation: Semmens, Rob, Nikolas Martelaro, Pushyami Kaveti, Simon Stent, and Wendy Ju. "Is Now A Good Time? An Empirical Study of Vehicle-Driver Communication Timing." In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-12. 2019.

TRI Authors: Muratahan Aykol,Linda Hung, Santosh Suram, Patrick Herring, Jens S. Hummelshoj

All Authors: Muratahan Aykol, Vinay I. Hegde, Linda Hung, Santosh Suram, Patrick Herring, Chris Wolverton, Jens S. Hummelshoj

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis. 

Citation: Aykol, Muratahan, Vinay I. Hegde, Linda Hung, Santosh Suram, Patrick Herring, Chris Wolverton, and Jens S. Hummelshøj. "Network analysis of synthesizable materials discovery." Nature communications 10, no. 1 (2019): 1-7.

TRI Author: Santosh Suram

All Authors: Carla P. Gomes, Junwen Bai, Yexiang Xue, Johan Björck, Brendan Rappazzo, Sebastian Ament, Richard Bernstein, Shufeng Kong, Santosh K. Suram, R. Bruce van Dover, and John M. Gregoire

We introduce CRYSTAL, a multi-agent AI system for crystal-structure phase mapping. CRYSTAL is the first system that can automatically generate a portfolio of physically meaningful phase diagrams for expert-user exploration and selection. CRYSTAL outperforms previous methods to solve the example Pd-Rh-Ta phase diagram, enabling the discovery of a mixed-intermetallic methanol oxidation electrocatalyst. The integration of multiple data-knowledge sources and learning and reasoning algorithms, combined with the exploitation of problem decompositions, relaxations, and parallelism, empowers AI to supersede human scientific data interpretation capabilities and enable otherwise inaccessible scientific discovery in materials science and beyond.  Read More

Citation: Gomes, Carla P., Junwen Bai, Yexiang Xue, Johan Björck, Brendan Rappazzo, Sebastian Ament, Richard Bernstein et al. "CRYSTAL: a multi-agent AI system for automated mapping of materials' crystal structures." MRS Communications 9, no. 2 (2019): 600-608.

TRI Authors: Alex Alspach, Kunimatsu Hashimoto, Naveen Kuppuswarny and Russ Tedrake

All Authors: A. Alspach, K. Hashimoto, N. Kuppuswarny and R. Tedrake

Incorporating effective tactile sensing and mechanical compliance is key towards enabling robust and safe operation of robots in unknown, uncertain and cluttered environments. Towards realizing this goal, we present a lightweight, easy-to-build, highly compliant dense geometry sensor and end effector that comprises an inflated latex membrane with a depth sensor behind it. We present the motivations and the hardware design for this Soft-bubble and demonstrate its capabilities through example tasks including tactile-object classification, pose estimation and tracking, and nonprehensile object manipulation. We also present initial experiments to show the importance of high-resolution geometry sensing for tactile tasks and discuss applications in robust manipulation. Read More

Citation: Alspach, Alex, Kunimatsu Hashimoto, Naveen Kuppuswarny, and Russ Tedrake. "Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation." In 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 597-604. IEEE, 2019.

TRI Authors: Astrid Jackson, Brandon D. Northcutt

All Authors: Astrid Jackson, Brandon D. Northcutt, Gita Sukthankar

One of the advantages of teaching robots by demonstration is that it can be more intuitive for users to demonstrate rather than describe the desired robot behavior. However, when the human demonstrates the task through an interface, the training data may inadvertently acquire artifacts unique to the interface, not the desired execution of the task. Being able to use one's own body usually leads to more natural demonstrations, but those examples can be more difficult to translate to robot control policies. This paper quantifies the benefits of using a virtual reality system that allows human demonstrators to use their own body to perform complex manipulation tasks. We show that our system generates superior demonstrations for a deep neural network without introducing a correspondence problem. The effectiveness of this approach is validated by comparing the learned policy to that of a policy learned from data collected via a conventional gaming system, where the user views the environment on a monitor screen, using a Sony Play Station 3 (PS3) DualShock 3 wireless controller as input. Read more 

Citation: Jackson, Astrid, Brandon D. Northcutt, and Gita Sukthankar. "The benefits of immersive demonstrations for teaching robots." In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 326-334. IEEE, 2019.

TRI Author: Jens Hummelshøj

All Authors: Paul C Jennings, Steen Lysgaard, Jens Strabo Hummelshøj, Tejs Vegge, Thomas Bligaard

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations. Read More

Citation: Jennings, Paul C., Steen Lysgaard, Jens Strabo Hummelshøj, Tejs Vegge, and Thomas Bligaard. "Genetic algorithms for computational materials discovery accelerated by machine learning." npj Computational Materials 5, no. 1 (2019): 1-6.

TRI Author: Ryan M. Eustice

All Authors: Maani Ghaffari Jadidi, William Clark, Anthony Bloch, Ryan M. Eustice, and Jessy W. Grizzle

This paper reports on a novel formulation and evaluation of visual odometry from RGB-D images. Assuming a static scene, the developed theoretical framework generalizes the widely used direct energy formulation (photometric error minimization) technique for obtaining a rigid body transformation that aligns two overlapping RGB-D images to a continuous formulation. The continuity is achieved through functional treatment of the problem and representing the process models over RGB-D images in a reproducing kernel Hilbert space; consequently, the registration is not limited to the specific image resolution and the framework is fully analytical with a closed-form derivation of the gradient. We solve the problem by maximizing the inner product between two functions defined over RGB-D images, while the continuous action of the rigid body motion Lie group is captured through the integration of the flow in the corresponding Lie algebra. Energy-based approaches have been extremely successful and the developed framework in this paper shares many of their desired properties such as the parallel structure on both CPUs and GPUs, sparsity, semi-dense tracking, avoiding explicit data association which is computationally expensive, and possible extensions to the simultaneous localization and mapping frameworks. The evaluations on experimental data and comparison with the equivalent energy-based formulation of the problem confirm the effectiveness of the proposed technique, especially, when the lack of structure and texture in the environment is evident.  Read More

Citation: Ghaffari, Maani, William Clark, Anthony Bloch, Ryan M. Eustice, and Jessy W. Grizzle. "Continuous direct sparse visual odometry from RGB-D images." In RSS 2019. arXiv preprint arXiv:1904.02266 (2019).

TRI Authors: Muratahan Aykol, Patrick K. Herring

All Authors: Kristen A. Severson, Peter M. Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H. Chen, Muratahan Aykol, Patrick K. Herring, Dimitrios Fraggedakis, Martin Z. Bazant, Stephen J. Harris, William C. Chueh & Richard D. Braatz

Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Read More

Citation: Severson, Kristen A., Peter M. Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H. Chen et al. "Data-driven prediction of battery cycle life before capacity degradation." Nature Energy 4, no. 5 (2019): 383-391.