This talk will survey recent work to achieve multi-contact locomotion control of humanoid and legged robots. I will start by presenting some results on robust optimization-based control. We exploited robust optimization techniques, either stochastic or worst-case, to improve the robustness of Task-Space Inverse Dynamics (TSID), a well-known control framework for legged robots. We modeled uncertainties in the joint torques, and we immunized the constraints of the system to any of the realizations of these uncertainties. We also applied the same methodology to ensure the balance of the robot despite bounded errors in the its inertial parameters. Extensive simulations in a realistic environment show that the proposed robust controllers greatly outperform the classic one. Then I will present preliminary results on a new capturability criterion for legged robots in multi-contact. "N-step capturability" is the ability of a system to come to a stop by taking N or fewer steps. Simplified models to compute N-step capturability already exist and are widely used, but they are limited to locomotion on flat terrains. We propose a new efficient algorithm to compute 0-step capturability for a robot in arbitrary contact scenarios. Finally, I will present our recent efforts to transfer the above-mentioned techniques to the real humanoid robot HRP-2, on which we recently implemented joint torque control.
Organizers: Ludovic Righetti
Object grasping and manipulation is a crucial part of daily human activities. The study of these actions represents a central component in the development of systems that attempt to understand human activities and robots that are able to act in human environments.
Three essential parts of this problem are tackled in this talk: the perception of the human hand in interaction with objects, the modeling of human grasping actions and the refinement of the execution of a robotic grasp. The estimation of the human hand pose is carrried out with a markerless visual system that performs in real time under object occlusions. Low dimensional models of various grasping actions are created by exploiting the correlations between different hand joints in a non-linear manner with Gaussian Process Latent Variable Models (GPLVM). Finally, robot grasping actions are perfected by exploiting the appearance of the robot during action execution.
Enabling computers to understand human behavior has the potential to revolutionize many areas that benefit society such as clinical diagnosis, human computer interaction, and social robotics. A critical element in the design of any behavioral sensing system is to find a good representation of the data for encoding, segmenting, classifying and predicting subtle human behavior.
In this talk I will propose several extensions of Component Analysis (CA) techniques (e.g., kernel principal component analysis, support vector machines, spectral clustering) that are able to learn spatio-temporal representations or components useful in many human sensing tasks. In the first part of the talk I will give an overview of several ongoing projects in the CMU Human Sensing Laboratory, including our current work on depression assessment from video, as well as hot-flash detection from wearable sensors.
In the second part of the talk I will show how several extensions of the CA methods outperform state-of-the-art algorithms in problems such as temporal alignment of human motion, temporal segmentation/clustering of human activities, joint segmentation and classification of human behavior, facial expression analysis, and facial feature detection in images. The talk will be adaptive, and I will discuss the topics of major interest to the audience.
Semiconductor light emitters, based on single crystal epitaxial inorganic semiconductor heterostructures are ubiquitous. In spite of their extraordinary versatility and technological maturity, penetrating the full visible spectrum using a single material system for red, green, and blue (RGB) in a seamless way remains, nonetheless, an elusive challenge.
Semiconductor nanocrystals, or quantum dots (QDs), synthesized by solution-based methods of colloidal chemistry represent a strongly contrasting basis for active optical materials. While possessing an ability to absorb and efficiently luminesce across the RGB by simple nanocrystal particle size control within a single material system, these preparations have yet to make a significant impact as viable light emitting devices, mainly due to the difficulties in casting such materials from their natural habitat, that is “from the chemist’s bottle” to a useful solid thin film form for device use. In this presentation we show how II-VI compound nanocrystals can be transitioned to solid templates with targeted spatial control and placeme
Invasive access by microprobe arrays inserted safely into the brain is now enabling us to “listen” to local neural circuits at levels of spatial and temporal detail which, in addition to enriching fundamental brain science, has led to the possibility of a new generation of neurotechnologies to overcome disabilities due to a range of neurological injuries where pathways from the brain to the rest of the central and peripheral nervous systems have been injured or severed. In this presentation we discuss the biomedical engineering challenges and opportunities with these incipient technologies, with emphasis on implantable wireless neural interfaces for communicating with the brain.
A second topic, related to the possibility of sending direct inputs of information back to the brain by implanted devices is also explored, focusing on recently discovered means to render selected neural cell types and microcircuits to be light sensitized, following local microbiologically induced conditioning.
The scope of this work is hand-object interaction. As a starting point, we observe hands manipulating objects and derive information based on computer vision methods. After considering hands and objects in isolation, we focus on the inherent interdependencies. One application of the gained knowledge is the synthesis of interactive hand motion for animated sequences.
Vision is a crucial sense for computational systems to interact with their environments as biological systems do. A major task is interpreting images of complex scenes, by recognizing and localizing objects, persons and actions. This involves learning a large number of visual models, ideally autonomously.
In this talk I will present two ways of reducing the amount of human supervision required by this learning process. The first way is labeling images only by the object class they contain. Learning from cluttered images is very challenging in this weakly supervised setting. In the traditional paradigm, each class is learned starting from scratch. In our work instead, knowledge generic over classes is first learned during a meta-training stage from images of diverse classes with given object locations, and is then used to support learning any new class without location annotation. Generic knowledge helps because during meta-training the system can learn about localizing objects in general. As demonstrated experimentally, this approach enables learning from more challenging images than possible before, such as the PASCAL VOC 2007, containing extensive clutter and large scale and appearance variations between object instances.
The second way is the analysis of news items consisting of images and text captions. We associate names and action verbs in the captions to the face and body pose of the persons in the images. We introduce a joint probabilistic model for simultaneously recovering image-caption correspondences and learning appearance models for the face and pose classes occurring in the corpus. As demonstrated experimentally, this joint `face and pose' model solves the correspondence problem better than earlier models covering only the face.
I will conclude with an outlook on the idea of visual culture, where new visual concepts are learned incrementally on top of all visual knowledge acquired so far. Beside generic knowledge, visual culture includes also knowledge specific to a class, knowledge of scene structures and other forms of visual knowledge. Potentially, this approach could considerably extend current visual recognition capabilities and produce an integrated body of visual knowledge.
I will survey our work on tracking and measurement, waypoints on the path to activity recognition and understanding, in sports video, highlighting some of our recent work on rectification and player tracking, not just in hockey but more recently in basketball, where we have addressed player identification both in a fully supervised and semi-supervised manner.
Methods for visual recognition have made dramatic strides in recent years on various online benchmarks, but performance in the real world still often falters. Classic gradient-histogram models make overly simplistic assumptions regarding image appearance statistics, both locally and globally. Recent progress suggests that new learning-based representations can improve recognition by devices that are embedded in a physical world.
I'll review new methods for domain adaptation which capture the visual domain shift between environments, and improve recognition of objects in specific places when trained from generic online sources. I'll discuss methods for cross-modal semi-supervised learning, which can leverage additional unlabeled modalities in a test environment.
Finally as time permits I'll present recent results learning hierarchical local image representations based on recursive probabilistic topic models, on learning strong object color models from sets of uncalibrated views using a new multi-view color constancy paradigm, and/or on recent results on monocular estimation of grasp affordances.
In the first part of the talk, I will describe methods that learn a single family of detectors for object classes that exhibit large within-class variation. One common solution is to use a divide-and-conquer strategy, where the space of possible within-class variations is partitioned, and different detectors are trained for different partitions.
However, these discrete partitions tend to be arbitrary in continuous spaces, and the classifiers have limited power when there are too few training samples in each subclass. To address this shortcoming, explicit feature sharing has been proposed, but it also makes training more expensive. We show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved in a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. The multiplicative kernel formulation enables feature sharing implicitly; the solution for the optimal sharing is a byproduct of SVM learning.
The resulting detector family is tuned to specific variations in the foreground. The effectiveness of this framework is demonstrated in experiments that involve detection, tracking, and pose estimation of human hands, faces, and vehicles in video.