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ORGANIZER;CN=Australian Centre For Robotics Administration;SENT-BY="mailto:s
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ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='academics
 -meeting@acfr.usyd.edu.au':mailto:academics-meeting@acfr.usyd.edu.au
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DESCRIPTION;LANGUAGE=en-US:Faculty of Engineering\n\n\nACFR Seminar Series\
 n[https://d31hzlhk6di2h5.cloudfront.net/20260705/44/a0/ac/85/83de99ae1318d
 a0add393c81.png]\n[https://d31hzlhk6di2h5.cloudfront.net/20260705/90/62/a1
 /a0/e806283be685b4d065bc82ba.jpg]\n\nYale Robotics Talks:\n1) Manipulating
  Uncertainty: Towards Reliable In-the-wild Dexterity with Less Data\n2) Ge
 neralizing Ergodic Coverage for Real-World Robotics\n\nPresented by Hrishi
 kesh Sathyanarayan & Christian Hughes\nDate: Thursday\, 9 July 2026\, at 1
 :00 pm AEST\nVenue: ACFR seminar area\, J04 level 2 (Rose St Building<http
 s://url.au.m.mimecastprotect.com/s/jPY5C81V0PTzqXkBDhgckfyOd8u?domain=t.e2
 ma.net>)\nZoom ID: https://uni-sydney.zoom.us/s/87306457455<https://url.au
 .m.mimecastprotect.com/s/2tteC91WPRTMqzOrLFKflfqC5zL?domain=t.e2ma.net>\n\
 n\nAbstract:\n\nManipulating Uncertainty: Towards Reliable In-the-wild Dex
 terity with Less Data\n\nRobotic manipulation is increasingly expected to 
 achieve reliable operation in uncertain environments such as kitchens\, wa
 rehouses\, and homes\, yet performance in these settings remains highly se
 nsitive to latent physical uncertainties that are often difficult to know 
 in advance.\nThis challenge is especially prominent in contact-rich tasks\
 , where uncertainty in friction\, mass\, geometry\, inertia\, or complianc
 e can amplify large differences in how objects move\, slip\, stick\, or de
 form during interaction. Modern robot learning has made significant progre
 ss by using large-scale data to reduce and adapt to uncertainty during tra
 ining\, but such methods often require far more data than necessary when t
 hey cannot deliberately target contact interactions that reveal the physic
 al uncertainties that are most crucial for reliable manipulation performan
 ce. In this talk\, I argue that the central challenge in manipulation is n
 ot merely the presence of physical uncertainty\, but rather the lack of pr
 incipled methods for deciding which uncertainties must be reduced\, repres
 ented\, or accounted for to achieve reliable dexterity. My work asks wheth
 er robots can become more deliberate about uncertainty by learning what is
  worth knowing\, what can be ignored for the task at hand\, and what must 
 be accounted for during control. I will present data-efficient methods for
  uncertainty-aware manipulation learning and control\, showing how robots 
 can actively exploit contact dynamics to achieve strong learning performan
 ce from only a small number of informative interactions. Finally\, I will 
 discuss how explicit reasoning over task-relevant physical uncertainty ena
 bles robots to solve manipulation tasks reliably without requiring a fully
  precise model of the world.\n\n\n\nGeneralizing Ergodic Coverage for Real
 -World Robotics\n\nAs robotic exploration extends into new domains\, robot
 s are entrusted with missions under increasingly strict time and energy co
 nstraints\, where success depends on prioritizing search according to each
  region’s importance. However\, in settings like search-and-rescue\, whe
 re success is critical\, prioritized exploration must come with a formal g
 uarantee that no region is left unexplored. Ergodic exploration offers a s
 olution to this problem by guaranteeing full coverage with time spent in p
 roportion to each region’s value\, but existing methods are limited to s
 tatic\, well-defined domains. In this talk\, I show how to extend these gu
 arantees to domains of arbitrary geometry\, at any scale\, over unbounded 
 time-horizons\, and in environments that evolve as the robot explores\, so
  that provable\, importance-aware coverage becomes practical in the missio
 ns where it matters most.\n\nBio:\n\n\nHrishikesh Sathyanarayan\n\nHrishik
 esh Sathyanarayan is a PhD candidate in Mechanical Engineering at Yale Uni
 versity\, and a member of the Computational Methods for Curious Robotics L
 ab\, advised by Professor Ian Abraham. His research interests include cont
 act-rich manipulation\, data-efficient robot learning\, and optimal contro
 l. Prior to Yale\, Hrishi completed his Bachelor’s degree in Aerospace E
 ngineering at Rutgers University.\n\n\n\nChristian Hughes\n\nChristian Hug
 hes is a PhD student in Mechanical Engineering at Yale University in the C
 omputational methods for Curious Robots Lab (CoCuRo Lab). Before joining t
 he CoCoRo Lab\, he earned his B.S. and M.S. in Aerospace Engineering from 
 Embry-Riddle Aeronautical University\, with concentrations in astronautics
  and in dynamics and control systems. Christian’s research designs struc
 tured formulations of optimization problems that come with provable guaran
 tees on the solution\, with much of his recent work focused on exploration
  and coverage. His research is motivated by the belief that trustworthy ro
 bots must offer guarantees\, because a system that usually works is not en
 ough when mission success is critical. Christian’s recent work focuses o
 n extending these guarantees to complex\, real-world domains where robots 
 actually operate.\n\n[Register for Event >]<https://url.au.m.mimecastprote
 ct.com/s/avDRC0YKPviMBkyYqFRh9f9AK6x?domain=t.e2ma.net>\n[https://d31hzlhk
 6di2h5.cloudfront.net/20260705/2e/8d/c7/1c/1c70baef8b350e3840963efe.png]\n
 \n[Web Site]<https://url.au.m.mimecastprotect.com/s/yNGpCjZ1N7i3mYOZKF4sKf
 mWHOi?domain=t.e2ma.net>\n[LinkedIn]<https://url.au.m.mimecastprotect.com/
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SUMMARY;LANGUAGE=en-US:ACFR Seminar Series: Hrishikesh Sathyanarayan & Chri
 stian Hughes\; Manipulating Uncertainty: Towards Reliable In-the-wild Dext
 erity with Less Data & Generalising Ergodic Coverage for Real-World Roboti
 cs
DTSTART;TZID=AUS Eastern Standard Time:20260709T130000
DTEND;TZID=AUS Eastern Standard Time:20260709T140000
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DTSTAMP:20260706T011914Z
TRANSP:OPAQUE
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