000			03408nam a22006015i 4500
001			978-3-031-37832-4
003			DE-He213
005			20240207153544.0
007			cr nn 008mamaa
008			230801s2023 sz | s |||| 0|eng d
020			_a9783031378324 _9978-3-031-37832-4
050		4	_aTJ212-225
050		4	_aTJ210.2-211.495
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
072		7	_aTJFM _2thema
082	0	4	_a629.8 _223
100	1		_aLutter, Michael. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aInductive Biases in Machine Learning for Robotics and Control _h[electronic resource] / _cby Michael Lutter.
250			_a1st ed. 2023.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2023.
300			_aXV, 119 p. 23 illus., 20 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Tracts in Advanced Robotics, _x1610-742X ; _v156
500			_aAcceso multiusuario
505	0		_aIntroduction -- A Differentiable Newton-Euler Algorithm for Real-World Robotics -- Combining Physics and Deep Learning for Continuous-Time Dynamics Models -- Continuous-Time Fitted Value Iteration for Robust Policies -- Conclusion.
520			_aOne important robotics problem is "How can one program a robot to perform a task"? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.
541			_fUABC ; _cPerpetuidad
650		0	_aControl engineering.
650		0	_aRobotics.
650		0	_aAutomation.
650		0	_aComputational intelligence.
650	1	4	_aControl, Robotics, Automation.
650	2	4	_aComputational Intelligence.
650	2	4	_aRobotics.
650	2	4	_aControl and Systems Theory.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031378317
776	0	8	_iPrinted edition: _z9783031378331
776	0	8	_iPrinted edition: _z9783031378348
830		0	_aSpringer Tracts in Advanced Robotics, _x1610-742X ; _v156
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-37832-4
912			_aZDB-2-INR
912			_aZDB-2-SXIT
942			_cLIBRO_ELEC
999			_c261319 _d261318