How to. write a paper. The basics writing a solid paper Different communities/different standards Common errors

Transcription

1 How to write a paper The baic writing a olid paper Different communitie/different tandard Common error

2 Reource Raibert eay My grammar point Article on a v. the Bug in writing

3 Clarity Goal Conciene Calling out the contribution very clearly

4 Section Abtract Introduction Background Overview Method Section 1-N Reult Dicuion Concluion

5 Section Order of writing Overview Method 1-N Background Dicuion Abtract Introduction Reult Concluion Order in paper Abtract Introduction Background Overview Method 1-N Reult Dicuion Concluion

6 Write an Outline firt Topic entence for each paragraph for the whole paper or a lit of point that you want to make in the paper, aigned to ection. Sketch out all the figure What are the contribution/key idea? What i the graph that would convince me that that i true?

7 Title Convey what the paper i about Branding from a catchy title Should turn up in a earch of keyword

8 Abtract Introduction Motivation: why did you do thi work? Method: What did you do? (key idea) Background: Briefly ituate work Reult: How demontrated? Dicuion: Evaluation

9 Abtract Deigning a unified framework for imulating multiple human behavior ha proven very difficult. In thi paper, we preent an approach for control ytem deign that can generate animation that cloely reemble a divere et of captured reference motion: walking, running, and gymnatic behavior. We achieve thi generalization with a balancing trategy that relie on an enhanced inverted pendulum model, which we call the momentum-mapped inverted pendulum model (MMIPM). Unlike a traditional inverted pendulum model, thi model handle flight phae when no foot i on the ground and take into account the momentum of the ytem. We analyze the reference motion in a pre-proceing tep to compute the motion of the MMIPM. At run-time, the controller ue the current etimate of the pendulum tate and a predicted pendulum trajectory to create a character that dynamically balance, change peed, make turn, jump and perform a backflip. The initial controller can be optimized to further improve the motion quality. We demontrate the power of thi approach by generating controller that produce natural looking animation and are robut to change in the environment, a variety of uer command and external diturbance.

10 Abtract Deigning a unified framework for imulating multiple human behavior ha proven very difficult. In thi paper, we preent an approach for control ytem deign that can generate animation that cloely reemble a divere et of captured reference motion: walking, running, and gymnatic behavior. We achieve thi generalization with a balancing trategy that relie on an enhanced inverted pendulum model, which we call the momentum-mapped inverted pendulum model (MMIPM). Unlike a traditional inverted pendulum model, thi model handle flight phae when no foot i on the ground and take into account the momentum of the ytem. We analyze the reference motion in a pre-proceing tep to compute the motion of the MMIPM. At run-time, the controller ue the current etimate of the pendulum tate and a predicted pendulum trajectory to create a character that dynamically balance, change peed, make turn, jump and perform a backflip. The initial controller can be optimized to further improve the motion quality. We demontrate the power of thi approach by generating controller that produce natural looking animation and are robut to change in the environment, a variety of uer command and external diturbance.

11 Abtract Deigning a unified framework for imulating multiple human behavior ha proven very difficult. In thi paper, we preent an approach to control ytem deign that can generate animation that cloely reemble a divere et of captured reference motion including walking, running, and gymnatic behavior. We achieve thi generalization with a balancing trategy that relie on an enhanced inverted pendulum model, which we call the momentum-mapped inverted pendulum model (MMIPM). Unlike a traditional inverted pendulum model, thi model handle flight phae when no foot i on the ground and take into account the momentum of the ytem. We analyze the reference motion in a pre-proceing tep to compute the motion of the MMIPM. At run-time, the controller ue the current etimate of the pendulum tate and a predicted pendulum trajectory to create a character that dynamically balance, change peed, make turn, jump and perform a backflip. The initial controller can be optimized to further improve the motion quality. We demontrate the power of thi approach by generating controller that produce natural looking animation and are robut to change in the environment, a variety of uer command and external diturbance.

12 Abtract Deigning a unified framework for imulating multiple human behavior ha proven very difficult. In thi paper, we preent an approach for control ytem deign that can generate animation that cloely reemble a divere et of captured reference motion: walking, running, and gymnatic behavior. We achieve thi generalization with a balancing trategy that relie on an enhanced inverted pendulum model, which we call the momentum-mapped inverted pendulum model (MMIPM). Unlike a traditional inverted pendulum model, thi model handle flight phae when no foot i on the ground and take into account the momentum of the ytem. We analyze the reference motion in a pre-proceing tep to compute the motion of the MMIPM. At run-time, the controller ue the current etimate of the pendulum tate and a predicted pendulum trajectory to create a character that dynamically balance, change peed, make turn, jump and perform a backflip. The initial controller can be optimized to further improve the motion quality. We demontrate the power of thi approach by generating controller that produce natural looking animation and are robut to change in the environment, a variety of uer command and external diturbance.

13 Abtract Deigning a unified framework for imulating multiple human behavior ha proven very difficult. In thi paper, we preent an approach for control ytem deign that can generate animation that cloely reemble a divere et of captured reference motion: walking, running, and gymnatic behavior. We achieve thi generalization with a balancing trategy that relie on an enhanced inverted pendulum model, which we call the momentum-mapped inverted pendulum model (MMIPM). Unlike a traditional inverted pendulum model, thi model handle flight phae when no foot i on the ground and take into account the momentum of the ytem. We analyze the reference motion in a pre-proceing tep to compute the motion of the MMIPM. At run-time, the controller ue the current etimate of the pendulum tate and a predicted pendulum trajectory to create a character that dynamically balance, change peed, make turn, jump and perform a backflip. The initial controller can be optimized to further improve the motion quality. We demontrate the power of thi approach by generating controller that produce natural looking animation and are robut to change in the environment, a variety of uer command and external diturbance.

14 Introduction Motivation: why did you do thi work? Motivation: why i thi a hard problem? Method: What did you do? (key idea) Background: Briefly ituate work Reult: How demontrated? Dicuion: Evaluation Dicuion: Limitation Contribution

15 Background Opening paragraph on topic to be covered Serie of paragraph on each topic Relate to your work Do not lah and burn Make ure that you actually undertand the work (and have read the paper!).

16 Opening paragraph on topic to be covered Background Our work build on control algorithm developed in both robotic and computer graphic. In thi ection, we cover inverted pendulum model for both tracking and control with a particular emphai on thoe that have been extended to include additional dynamic term uch a momentum. Inverted Pendulum Model for tracking Inverted Pendulum Model for control Extended Inverted Pendulum Model

17 Background Relate to your work but do not retate what you did repeatedly X demontrated that an IPM could be ued effectively to do We build on their work by adding a momentum term to the calculation of the IPM, thu enabling a much wider variety of behavior.

18 Background Do not lah and burn X were the firt to do. X created the firt proof of concept that In an impreive piece of work given the compute power available at the time, X NOT: X work i limited in that Although unupported in the paper, X claimed that

19 Overview Outline of method in logical order. Sentence or paragraph hould correpond directly to the ection to come in Method. Roughly one paragraph for each entence about the method in the intro.

20 Method Follow order from Overview Be clear about what i new and what i from the literature (dynamic, for example).

21 Reult If the material i better repreented in video, jut put a few figure into the paper Make all your figure firt and then jut decribe them Do make ure that you lit all the tet cae that you have.

22 Evaluation Try to hold yourelf to the ame tandard that we have ued in the debate! Make ure that you believe your tat are good meaure Compare to previou publication where poible uing their code or their input file

23 Dicuion Firt paragraph can be a ummary Call out limitation here Try to tep back and reflect on the work worked on well-compoed image only optimization would be intractable for long equence

24 Concluion Yet another ummary of the paper Make ure that it differ from the abtract If it doen t add value, you can kip it

25 Figure and Video Put in the time to make them conitent and legible (not jut cut/pate from Matlab A video with a good explanation of the algorithm can really add to the paper

26 Different Communitie CV and ytem: related work ometime at the end CV: code on the web i almot required Robotic: paper are much horter and more incremental SIGGRAPH: alway have a teaer image

27 Common Error Read your proe aloud Reviewer management Spell check! Thi hould alway be followed by a noun Proper ue of article Since v. becaue Form of the verb to be don t get colon Sentence hould be complete without the reference