6,425 research outputs found
Data-Driven Approach to Simulating Realistic Human Joint Constraints
Modeling realistic human joint limits is important for applications involving
physical human-robot interaction. However, setting appropriate human joint
limits is challenging because it is pose-dependent: the range of joint motion
varies depending on the positions of other bones. The paper introduces a new
technique to accurately simulate human joint limits in physics simulation. We
propose to learn an implicit equation to represent the boundary of valid human
joint configurations from real human data. The function in the implicit
equation is represented by a fully connected neural network whose gradients can
be efficiently computed via back-propagation. Using gradients, we can
efficiently enforce realistic human joint limits through constraint forces in a
physics engine or as constraints in an optimization problem.Comment: To appear at ICRA 2018; 6 pages, 9 figures; for associated video, see
https://youtu.be/wzkoE7wCbu
Applying Cross-cultural theory to understand users’ preferences on interactive information retrieval platform design
Presented at EuroHCIR 2014, the 4th European Symposium on Human-Computer Interaction and Information Retrieval, 13th September 2014, at BCS London Office, Covent Garden, London.In this paper we look at using culture to group users and model the users’ preference on cross cultural information retrieval, in order to investigate the relationship between the user search preferences and the user’s cultural background. Initially we review and discuss briefly website localisation. We continue by examining culture and Hofstede’s cultural dimensions. We identified a link between Hofstede’s five dimensions and user experience. We did an analogy for each of the five dimensions and developed six hypotheses from the analogies. These hypotheses were then tested by means of a user study. Whilst the key findings from the study suggest cross cultural theory can be used to model user’s preferences for information retrieval, further work still needs to be done on how cultural dimensions can be applied to inform the search interface design
Learning a Unified Control Policy for Safe Falling
Being able to fall safely is a necessary motor skill for humanoids performing
highly dynamic tasks, such as running and jumping. We propose a new method to
learn a policy that minimizes the maximal impulse during the fall. The
optimization solves for both a discrete contact planning problem and a
continuous optimal control problem. Once trained, the policy can compute the
optimal next contacting body part (e.g. left foot, right foot, or hands),
contact location and timing, and the required joint actuation. We represent the
policy as a mixture of actor-critic neural network, which consists of n control
policies and the corresponding value functions. Each pair of actor-critic is
associated with one of the n possible contacting body parts. During execution,
the policy corresponding to the highest value function will be executed while
the associated body part will be the next contact with the ground. With this
mixture of actor-critic architecture, the discrete contact sequence planning is
solved through the selection of the best critics while the continuous control
problem is solved by the optimization of actors. We show that our policy can
achieve comparable, sometimes even higher, rewards than a recursive search of
the action space using dynamic programming, while enjoying 50 to 400 times of
speed gain during online execution
Metal-organic frameworks as selective or chiral oxidation catalysts
Since the discovery of Metal Organic Frameworks (MOFs) in the early 1990s, the amount of new structures has grown exponentially. A MOF typically consists of inorganic nodes that are connected by organic linkers to form crystalline, highly porous structures. MOFs have attracted a lot of attention lately, as the versatile design of such materials holds promises of interesting applications in various fields. In this review, we will focus on the use of MOFs as heterogeneous oxidation catalysts. MOFs are very promising candidates to replace homogeneous catalysts by sustainable and stable heterogeneous catalysts.
The catalytic active function can be either the active metal sites of the MOF itself or can be introduced as an extra functionality in the linker, a dopant or a "ship-in-a-bottle" complex. As the pore size, pore shape, and functionality of MOFs can be designed in numerous ways, shape selectivity, and even chiral selectivity can be created. In this article, we will present an overview on the state of the art of the use of MOFs as a heterogeneous catalyst in liquid phase oxidation reactions
Cu@COMOC-4, a bimetallic metal-organic framework for the size selective epoxidation of cycloalkenes
Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs
Traversing environments with arbitrary obstacles poses significant challenges
for bipedal robots. In some cases, whole body motions may be necessary to
maneuver around an obstacle, but most existing footstep planners can only
select from a discrete set of predetermined footstep actions; they are unable
to utilize the continuum of whole body motion that is truly available to the
robot platform. Existing motion planners that can utilize whole body motion
tend to struggle with the complexity of large-scale problems. We introduce a
planning method, called the "Randomized Possibility Graph", which uses
high-level approximations of constraint manifolds to rapidly explore the
"possibility" of actions, thereby allowing lower-level motion planners to be
utilized more efficiently. We demonstrate simulations of the method working in
a variety of semi-unstructured environments. In this context,
"semi-unstructured" means the walkable terrain is flat and even, but there are
arbitrary 3D obstacles throughout the environment which may need to be stepped
over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation
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