Topic Tag: motion

home Forums Topic Tag: motion

 First-Person Perceptual Guidance Behavior Decomposition using Active Constraint Classification

Humans exhibit a wide range of adaptive and robust dynamic motion behavior that is yet unmatched by autonomous control systems. These capabilities are essential for real-time behavior generation in cluttered environments. Recent work suggests that human capabilities rely on task structure learning …


 The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

 

The principle goal of computational mechanics is to define pattern and structure so that the organization of complex systems can be detected and quantified. Computational mechanics developed from efforts in the 1970s and early 1980s to identify strange attractors as the mechanism driving weak fluid…


 Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation

 

How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neura…


 Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis`

  

This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, insufficient toestimate an accurate reward function. Observing that each demonstrator has an inherent reward for each stat…


 Socially-compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning

We present an approach for mobile robots to learn to navigate in pedestrian-rich environments via raw depth inputs, in a social-compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy for motion planning, which improves upon a supervised policy model …


 A self-organizing neural network architecture for learning human-object interactions

The visual recognition of transitive actions comprising human-object interactions is a key component enabling artificial systems to operate in natural environments. This challenging task requires, in addition to the recognition of articulated body actions, the extraction of semantic elements from t…


 SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

 

In this work, we present an approach to deep visuomotor control using structured deep dynamics models. Our deep dynamics model, a variant of SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an encoder-decoder structure. Unlike prior work, our dynamics model is structured…


 Learning event representation: As sparse as possible, but not sparser

Selecting an optimal event representation is essential for event classification in real world contexts. In this paper, we investigate the application of qualitative spatial reasoning (QSR) frameworks for classification of human-object interaction in three dimensional space, in comparison with the u…


 Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF

  

Event learning is one of the most important problems in AI. However, notwithstanding significant research efforts, it is still a very complex task, especially when the events involve the interaction of humans or agents with other objects, as it requires modeling human kinematics and object movement…


 Human motion primitive discovery and recognition

 

We present a novel framework for the automatic discovery and recognition of human motion primitives from motion capture data. Human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which depends on the motion of a group of skeletal joints. Models of each prim…


 Heuristic Online Goal Recognition in Continuous Domains

Goal recognition is the problem of inferring the goal of an agent, based on its observed actions. An inspiring approach – plan recognition by planning (PRP) – uses off-the-shelf planners to dynamically generate plans for given goals, eliminating the need for the traditional plan library…


 Robust nonparametric nearest neighbor random process clustering

 

We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the $L^1$-distance between estimated pow…


 Underwater Multi-Robot Convoying using Visual Tracking by Detection

  

We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal …


 Multi-task Learning with Gradient Guided Policy Specialization

 

We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization training. During the joint training stage, a neural network policy is trained with minimal information to disambiguate the…


 Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

  

High-dimensional partial differential equations (PDE) appear in a number of models from the financial industry, such as in derivative pricing models, credit valuation adjustment (CVA) models, or portfolio optimization models. The PDEs in such applications are high-dimensional as the dimension corre…


 Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video

   

Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection per…


 Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation

 

Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While supervised learning optimises a model for the training domain, it will deliver degraded performance in application domains that…


 Latent Gaussian Process Regression

 

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance functi…


 Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions

We present a commonsense, qualitative model for the semantic grounding of embodied visuo-spatial and locomotive interactions. The key contribution is an integrative methodology combining low-level visual processing with high-level, human-centred representations of space and motion rooted in artific…


 Minimax Filter: Learning to Preserve Privacy from Inference Attacks

 

Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios, can be challenging with syntactic anonymization methods which are designed for discrete attributes. Differential privacy, which provides a more formal definition of privacy, has shown more success in sa…


 IDK Cascades: Fast Deep Learning by Learning not to Overthink

  

Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that for a majority of real-world inputs, the recent advances in deep learning have created models that effectively “ov…


 Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach

Operational maturity of biological control systems have fuelled the inspiration for a large number of mathematical and logical models for control, automation and optimisation. The human brain represents the most sophisticated control architecture known to us and is a central motivation for several …


 Community Recovery in Hypergraphs

 

Community recovery is a central problem that arises in a wide variety of applications such as network clustering, motion segmentation, face clustering and protein complex detection. The objective of the problem is to cluster data points into distinct communities based on a set of measurements, each…


 Recurrent Ladder Networks

  

We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and tempor…


 Neural Networks for Safety-Critical Applications – Challenges, Experiments and Perspectives

 

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-ba…